research on steganography pdf

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Digital image steganography and steganalysis: A journey of the past three decades

Steganography is the science and art of covert communication. Conversely, steganalysis is the study of uncovering the steganographic process. The evolution of steganography has been paralleled by the development of steganalysis. In this game of hide and seek, the two player’s steganography and steganalysis always want to break the other down. Over the past three decades, research has produced a plethora of remarkable image steganography techniques (ISTs). The major challenge for most of these ISTs is to achieve a fair balance between the metrics such as high hiding capacity (HC), better imperceptibility, and improved security. This study aims to present an exhaustive scrutiny of various ISTs from the classical to recent developments in the spatial domain, with respect to various image steganographic metrics. Further, the current status, recent developments, open challenges, and promising directions in this field are also highlighted.

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Distributed Steganography in PDF Files—Secrets Hidden in Modified Pages

This paper shows how to diffuse a message and hide it in multiple PDF files. Presented method uses dereferenced objects and secret splitting or sharing algorithms. It is applicable to various types of PDF files, including text documents, presentations, scanned images etc. Because hiding process is based on structure manipulation, the solution may be easily combined with content-dependent steganographic techniques. The hidden pages are not visible in typical application usage, which was tested with seven different programs.

1. Introduction

PDF is one of the most common used document formats nowadays. The main advantages of these files are preserving the appearance when the document is printed and using hyperlinks. On the other hand, PDF is not customizable and has many problems with changing a style, like increasing font size or adjusting contrast. Nevertheless, its popularity is growing together with the number of compatible software, including readers embedded in web browsers.

Not everyone knows that PDF files may hold secrets, too. Concealing data in an inconspicuous way is called steganography [ 1 ] and is mainly used to communicate secretly. This attribute differentiates it from cryptography [ 2 , 3 ]—when information is encrypted, it cannot be read, but everyone knows of its existence. In steganography, the primary goal is undetectability of secret message. This is realized with an ordinary file called carrier or container in which the message is embedded. Virtually any file may play this role, for example an image, video or HTML page [ 4 ].

Sometimes, the steganography is used solely; in other cases, it is combined with other means of information security [ 5 ]. The one example is to disperse the message in the container with steganographic key. Another is distributed steganography in which the secret is split into some shares that are later embedded in multiple carrier files. The files with payload may be saved in remote locations or in a cloud. To reveal the secret, required number of shares should be recovered first. The general scheme of this approach is presented in Figure 1 .

Distributed steganography scheme.

A few examples of steganographic algorithms designed to work on PDF files include: white text on white background, object overlapping or image steganography in existing pictures. These specialized solutions may be sufficient in some situations, but are not appropriate for many documents commonly found in real life applications, like pure text or scans. Presented steganographic method is more universal and may be used in any type of PDF file.

This paper describes how to split a payload and then conceal the shares in PDF files. Section 2 presents technical details of PDF format. Section 3 demonstrates general method of hiding pages in PDF documents with comprehensible example. Specific algorithms of secret splitting are shown in Section 4 . Details of extracting the data from documents may be found in Section 5 . The last section summarizes the entire article.

2. PDF Format

PDF (Portable Document Format) is a popular document format that became open standard in 2008 [ 6 ]. It is text-based, but may (and usually does) contain binary streams. PDFs are consisted of a few parts: header, body, cross-reference table, trailer, xref pointer and end of file marker [ 7 ], as presented in Figure 2 .

An example of simple PDF file and its parts. The lines on the scheme do not belong to the file; they were added for better readability.

The header of PDF starts with a signature—%PDF- followed by version. It may be PDF-1.5 as in presented example, but older or newer version are possible as well. Another information that is optional in header is charset identifier. This is used by software to determine if the file is encoded in ASCII or not. When the identifier is present, it appears in second line of the file as 4 non-ASCII characters in a comment. (The comments in PDF are created with %, so the header itself is a comment!)

The next and most important part of PDF file is body section. Here are stored all contents normally visible in PDF reader. The body is consisted of objects which come in many types: boolean values, numbers, strings, arrays, dictionaries, binary streams and others. They are built in the following manner:

Each object has its own unique identifier (number). Generation of object is usually equal to 0 (unless the file has multiple revisions) and content very often lies between << and >> which is dictionary format.

Crossref (xref) plays a role of table of contents of the file. Its initial line stores two values: offset and number of objects (in our example from Figure 2 it is 0 8 ). After that there is a table in which each row corresponds to a single object and includes its offset and generation. The table has fixed length of 20 characters so that is easy to parse. The important information to note is that the first object is always null.

The objects form tree structure, so the order of elements in xref table does not necessarily correspond to their appearance in the document. Obviously, the initial element needs to be stored somehow to inform the parser where the content starts. This is what trailer is used for—it defines the reference to root object together with its size (count of indirect objects). When the trailer is parsed, it gives the location of root object which then allows to subsequently parse linked objects called kids.

The second to last element of PDF is xref pointer that indicates where xref table is located. It occupies two lines: first with startxref keyword and second with single number denoting the offset to xref.

PDFs end with %%EOF which is end of file marker. The important but easy to miss fact is that the percent character in this line is doubled.

3. Hidden Pages

The whole page(s) of PDF document may be hidden by modifying the tree structure of objects. The one idea is to remove reference to a certain page from its parent and to decrease the counter of pages. The content of the object remains untouched; only the reference is deleted. Let us consider the following example:

This is a fragment from 2-pages PDF created to demonstrate concealing process. Presented object has two references to kids (id 3 and id 11) and the page counter is set to 2. To delete the second page, we need to remove the reference 11 0 R and additionally decrease page counter by 1. The resulting file is smaller than the original by 7 bytes (which is about 0.07% of total size). Here is how the object looks like after such modifications.

The new document was tested to see if it opens correctly and the number of pages was as expected. The tests included PDF readers that parse and show documents directly and also graphical software with ability to import PDF as raster or vector image. They were: Evince, Adobe Acrobat X, Okular, Firefox (web browser that may internally render PDFs)—group 1; GIMP, Inkscape and Draw from LibreOffice—group 2.

It turned out that every program was able to open the file and show total page count equal to 1 as it should. The only incident occurred during closing the document in Acrobat X. The application ask if it should save changes. But of course no changes were made, the file was just opened. Why this happened? Probably Acrobat X assumed that orphaned object was added to document in current session. If so, why a number of other applications processed this file normally and without any warnings? This is because most readers are written to be tolerant to various weirdness that sometimes appear in PDF files. There are even programs that accept malformed documents, for example with some elements missing. In this study Acrobat X was found the most strict software.

Another approach is applicable when pages are removed from the end of the file. This method is even simpler than previous one, as all we need to do is to decrease the page counter. Then the object would look like:

In this case the reference was not cleared, so only 1 byte was modified and the file size did not change at all.

The tests from previous version were repeated on newly created document. Again, the document opened correctly in each program and the number of pages was equal to 1. This time Acrobat X did not show any dialog (as well as remaining applications) and no warnings were found. Lastly, new PDF was checked with command line program pdftk that was asked to extract the second page from the document. It raised the error that the input file has only 1 page—which confirmed correctness of the solution.

To sum up this section, both methods produce PDF documents with correct number of pages. Second technique (without clearing reference) was found more silent. The size of resulting file is identical or almost identical as the source PDF. Also, described algorithm of secret hiding works very efficiently in terms of complexity. No matter how many pages are concealed, counter modification is done in constant time.

3.1. Example of Adding and Hiding a Page in PDF

Presented technique is particularly useful when a new page (to be hidden) is added to preexisting PDF document. It requires two files: one carrier and one PDF with secret content. First the files are combined and then new pages are concealed as described earlier.

A comprehensible example demonstrate the whole process step by step. At the beginning, two files were prepared—simple 2-pages “Hello World” document that served as a carrier and secret document with text next to a picture.

Next, the documents were joined with pdftk (pdftoolkit):

where ex1.pdf and ex2.pdf are input files and ex3.pdf is final document. Other methods of combining are also possible, such as pdfjam or various PDF printers. The resulting file had 3 pages, two from the container and one from secret document.

Later secret content was hidden with decreasing total page counter. Indeed, the final document has only 2 pages, as presented in Figure 3 . The screenshots show files opened in Firefox, but tests conducted with different software gave the same results.

Example of adding and hiding secret page in PDF document.

The intermediate file was somewhat smaller than sum of carrier and secret file. This is because joining is not simple concatenation, but introduction of new objects and modification of crossref table. The final document had exactly the same length as the intermediate one, which was expected regarding hiding process as byte substitution.

Presented technique does not leave visual traces that user may accidentally bump into during normal application usage. This differentiate it from other known techniques, as setting text color identical to background or hiding the text behind another object. Figure 4 presents what happens when the hidden text is selected, either manually or with Ctrl+A. This is common practice if the user wants to copy an excerpt or when there is insufficient contrast and the content is hard to read. Normally both texts are invisible (one on the left is white, one on the right is covered with white rectangle), but with selection they are easy to spot. Such situation does not occur in described method which is based on structure manipulation.

Example of different technique—hiding text by changing color or covering it with a rectangle.

4. Secret Sharing

Secret sharing techniques allow to divide a secret into several parts called shares that can later be joined to recover initial information [ 8 ]. Usually a certain number of shares is required to successfully reconstruct the secret [ 9 ]. This value is called threshold and it may be lower than total number of shares. In such case secret message may be reconstructed even when some parts are missing. It is also possible to create more complex solutions, including hierarchical [ 10 ], expandable [ 11 , 12 ] or multistage systems [ 13 ]. A majority of schemes use numeric secrets, but there are exceptions, for example matrices [ 14 , 15 ] or images [ 16 ].

The most known secret sharing algorithm is based on XOR operation. It provides perfect secrecy (any number of shares below the threshold is unable to find anything about the secret), but all shares are required to reconstruct secret message. For two parts, the first share is random binary data of length equal to secret length; the second share is a result of bitwise XOR operation on random data and the secret. The reconstruction is simply XOR of both shares.

XOR secret sharing is easy to demonstrate with text data that may be represented in ASCII, Unicode or other encoding standard. For example, converting “Hello” to ASCII codes gives 72, 101, 108, 108, 111 . These numbers take part in share generation. Let us assume that we want three shares, so we need to create two sets of random numbers (of length equal to input text length, in this case 5). The values should be limited to range 0–255 as chosen encoding requires. The random sets are 20, 155, 255, 35, 218 and 38, 106, 229, 76, 174 —they serve as share1 and share2. The last share is created as XOR of ASCII codes of “Hello” and two sets of random values, which is equal to 122, 148, 118, 3, 27 . Now, to reconstruct initial text, one need to compute elementwise XOR of all three shares and transform the codes back to characters. This process is not presented here, but everyone is invited to test correctness of the algorithm with own hands.

In XOR method every share is necessary to recover the secret, which may be seen as disadvantage when some shares are missing or destroyed. In such cases schemes with threshold are the best solution. They have two parameters: n —total number of shares and k —number of shares sufficient to reconstruct the secret. An example of threshold system is encoding the secret in a polynomial. With n = 4 and k = 3, we need to create a polynomial of degree 2 and evaluate it at 4 chosen values. For instance, the secret is equal to 10 and our polynomial is p ( x ) = − 4 x 2 + 2 x + 10 (the secret is free term, remaining coefficients are random numbers). We choose 4 points that lie on polynomial: ( 1 , 8 ) , ( 2 , − 2 ) , ( − 1 , 4 ) and ( − 2 , − 10 ) —these are shares. To recover the secret, one need to reconstruct the polynomial from any three points (which is possible even if one of four points is missing) and get free term. Obviously polynomial formula is shredded after share generation and cannot be used to read the secret directly.

Many more secret sharing algorithms are available besides presented above [ 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ]. They offer various secret formats, number of shares and other characteristics that may be adjusted to needs.

5. Distributed Steganography on Hidden Pages in PDF

Previous sections explained how to conceal a page in PDF file and how to divide a secret into pieces. In distributed steganography both these operations are used. Each share is embedded in separate document by placing the payload at additional page which is later removed (as presented in Section 3.1 ). In reconstruction phase, shares are combined to form the secret. But in between there is one more thing to discuss: hidden data recovery.

To explain that, a few details of PDF processing will be necessary. The data in PDF files are stored in binary streams that are easy to identify: they lie between stream and endstream keywords. The content of the stream may be additionally encoded (which is optional, but very often used due to compression) or encrypted. An object with stream may declare many encodings which are applied subsequently. In such case, to retrieve final data, multiple decoders need to be used one by one.

When the document is processed, the software is able to identify required decoders because of the property /Filter followed by decoder name. The most common is /FlateDecode (zlib compression), but others like /ASCIIHexDecode are also possible.

5.1. Example of Decoding a Secret from Multiple PDF Files

To show presented ideas in practice, we will go step by step through decoding process. The message is split with XOR technique (described in Section 4 ) and hidden in two documents filled with “Lorem ipsum” text (2 and 5 pages). First we need to locate the removed page and follow the references until reaching the object with binary stream. In first file its content was (non-printing characters are in hex):

The object declared FlateDecode, therefore the next step is to decompress these data with zlib. At this stage the interesting parts are already visible:

From that we can easily obtain pure numbers: 11, 239, 177, 117, 236 .

The same process we may repeated for second file. Here is binary content of the stream:

After zlib decompression:

And just numbers: 67, 138, 221, 25, 131 .

The one final step is needed to decode hidden message. Two arrays should be XORed elementwise and casted to characters. Eventually the secret is revealed: Hello .

6. Detection

It is also important to analyze possible attacks aimed at detecting or destroying hidden data. When the original file is present, an adversary may use diff program to find discrepancies. However, PDF documents contain binary streams, so they are automatically treated as binaries. Normally, when diff compares binaries, it only informs whether the files differ or not, without giving any details or showing interesting fragments. This is why the adversary needs to use --text option to compare PDF documents line by line.

The program deals well with this task, especially in ideal case when only the counter was modified ( Figure 5 ). These are files that were analyzed in Section 5.1 . The output shows how exactly they differ. For example, “287c287” means that line 287 from first file need to be changed (c) to match line 287 from second file. The lines themselves are separated with “ - - - ” and each of them is marked with “ < ” or “ > ” depending of origin.

Output of diff program for modified files in ideal case.

Such laboratory conditions obviously do not apply to real life in which source file is not conveniently available. Then the adversary needs to perform deeper analysis, including tree structure examination. One possibility is to look for orphaned objects (those which cannot be reached from the root). This strategy is eventually able to find objects deleted from Kids references. On the other hand, it is useless when only the counter was decreased and the references are present. So the attacker should also check pages table and counter value. Different approaches, like manual examination of objects, are more probable to fail. In PDF documents numerous objects represent fonts and page elements, so finding actual content may be challenging.

Incidentally, detecting additional objects or other errors in PDF file does not necessarily mean that steganography was used. Sometimes documents are malformed, have some parts missing or present other weirdness. But the software is still able to process those files and display them more or less correctly. The question may arise why invalid documents are accepted, usually without any warning? The answer is simple: if the reader forced precise adjustment to standard, some documents would break and users would switch to another program that supports not-so-correct format (speaking of which, the same problem is present in HTML browsers). A positive side of that mess is the chance to occasionally use steganography in PDF documents.

Another method of steganalysis is file size examination. This technique is efficient in very short files or when payload size is significant in relation to carrier document size. Of course in serious applications such situation is unacceptable—when size of the file changes noticeably, another container should be selected. Nevertheless, the method is mentioned as theoretical mean which may be used against inexperienced steganogaphers.

Presented technique is resistant to attacks targeted at file content (like resampling and replacing an image) because it is structure-based. The resistance to joining PDF documents varies depending on the software used. It was tested with two applications: pdftk and pdfmerge. The interesting thing is that pdftk behaves differently when working on files created with various version of the algorithm. In case of simple counter decrement it leaks share content, but when the references are deleted, the shares are not compromised (they may be removed, though). These observations apply regardless of order of concatenated documents. On the other hand, pdfmerge raises an error when at least one of the input files is modified, but nonetheless creates valid output without revealing hidden shares.

Destroying hidden data is separate type of attack that is aimed to remove the payload, not to detect it. In typical blind usage suspicious file is processed regardless whether it contains secret data or not. This approach may be used when the amount of data is so massive that it exceeds the ability of inspection or when other methods fail. Such attacks give quite good results in image steganography, because human eye is unable to notice slight alterations of pixels. However, the same cannot be said about binary data. Blind modification or deletion of random streams may damage the file, change its appearance or destroy important content. In conclusion, blind attacks to PDF documents usually make those files useless—for this reason are not recommended.

The concept presented in this paper has been shown on some short documents to be easy to understand. But the analysis reached also larger PDF files, including over 300 MB file created from scanned images. Regardless of page dimensions or dominating types of objects, the hiding and recovering processes were successful. This means that different kinds of PDF documents may serve as containers (scanned files, presentations, text documents, etc.), which is a huge advantage of presented method. Also the secret length may vary. The tests proved that it is possible to conceal a secret on single page as well as on multiple pages. So the universality is main factor that distinguishes this technique from others and allows to build upon it for further advancements.

The solution was analyzed with 7 different programs: 4 PDF readers (Evince, Adobe Acrobat X, Okular, Firefox—web browser) and 3 graphical applications with ability to render PDF content (GIMP, Inkscape and Draw from LibreOffice). Created files opened correctly in all of them; the approach of decrement the counter turned out to be better, because the software had not shown any trace of hidden content.

In real world applications the circumstances should also be considered to make detection harder. The recommended way is to use common documents of decent length that may easily blend in normal traffic. Although the capacity of presented method is theoretically unlimited, it is advisable to choose carriers so that the share size does not exceed 1% (cautious variant) to 5% (more risky) of carrier size. It is particularly important when the shares are not textual, but for example in graphical format.

PDF files have been chosen to this study because of their wide use in Internet which make them great steganographic medium. Obtained results encourage to conduct more research in this area. The future projects may concern new methods of distributed steganography in PDF documents, incorporating cryptography and other means of security. Another idea is to extend the study by new types of carriers and develop advanced methods of sharing and concealing secrets. This covers a broad area of research and concerns especially combination of steganography and secret sharing [ 25 , 26 ]. There are still much things to discover and open problems to address in fascinating field of information hiding.

Abbreviations

The following abbreviations are used in this manuscript:

Author Contributions

Methodology, K.K.; software, K.K.; testing, K.K. and M.R.O.; validation, M.R.O.; investigation, K.K.; writing–original draft preparation, K.K.; writing–review and editing, M.R.O.; visualization, K.K.; supervision, M.R.O.; project administration, K.K.; funding acquisition, K.K. and M.R.O. All authors have read and agreed to the published version of the manuscript.

This work has been supported by the AGH University of Science and Technology research Grant No 16.16.120.773.

Conflicts of Interest

The authors declare no conflict of interest.

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Computer Science > Cryptography and Security

Title: steganography of steganographic networks.

Abstract: Steganography is a technique for covert communication between two parties. With the rapid development of deep neural networks (DNN), more and more steganographic networks are proposed recently, which are shown to be promising to achieve good performance. Unlike the traditional handcrafted steganographic tools, a steganographic network is relatively large in size. It raises concerns on how to covertly transmit the steganographic network in public channels, which is a crucial stage in the pipeline of steganography in real world applications. To address such an issue, we propose a novel scheme for steganography of steganographic networks in this paper. Unlike the existing steganographic schemes which focus on the subtle modification of the cover data to accommodate the secrets. We propose to disguise a steganographic network (termed as the secret DNN model) into a stego DNN model which performs an ordinary machine learning task (termed as the stego task). During the model disguising, we select and tune a subset of filters in the secret DNN model to preserve its function on the secret task, where the remaining filters are reactivated according to a partial optimization strategy to disguise the whole secret DNN model into a stego DNN model. The secret DNN model can be recovered from the stego DNN model when needed. Various experiments have been conducted to demonstrate the advantage of our proposed method for covert communication of steganographic networks as well as general DNN models.

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Byakugan – The Malware Behind a Phishing Attack

Affected Platforms: Microsoft Windows Impacted Users: Microsoft Windows Impact: The stolen information can be used for future attack Severity Level: High

In January 2024, FortiGuard Labs collected a PDF file written in Portuguese that distributes a multi-functional malware known as Byakugan. While investigating this campaign, a report about it was published. Therefore, this report will only provide a brief analysis of the overlap between that attack and this and focus primarily on the details of the infostealer.

Infection Vector

The PDF image shows a blurred table and asks the victim to click the malicious link on the PDF file to see the content. Once the link is clicked, a downloader is downloaded. The downloader drops a copy of itself (require.exe) along with a clean installer to the temp folder. It then downloads a DLL (dynamic link library), which is executed via DLL-hijacking to run require.exe to download the main module (chrome.exe). It executes the downloader's copy (require.exe), not the downloader (Reader_Install_Setup.exe), because when the downloader is named "require.exe" and located in the temp folder, its behavior is different from when it is Reader_Install_Setup.exe.

The downloader downloads Byakugan’s main module from thinkforce[.]com[.]br. This is the C2 server from which Byakugan receives files and commands. However, it may also work as the attacker's control panel. There is a login page on port 8080. We found descriptions of its features from the page's source code.

Byakugan is a node.js-based malware packed into its executable by pkg. In addition to the main script, there are several libraries corresponding to features.

Additionally, Byakugan can download extra files to perform its functions. These are stored in the default base path, % APPDATA%ChromeApplication, which is also used to store data created by Byakugan.

Byakugan has the following features:

• Screen monitor Lib: streamer.js It uses OBS Studio to monitor the victim’s desktop.

In a previous variant (7435f11e41735736ea95e0c8a66e15014ee238c3a746c0f5b3d4faf4d05215af), Byakugan downloaded the software from its domain. But this is not seen in this newer variant.

• Screen capture Lib: api.js Takes screenshots using Windows APIs.

• Miner Lib: miner.js The attacker can decide whether or not to continue mining when the victim is playing highly demanding games, which can impact performance. The attacker can also choose between mining with a CPU or a GPU to prevent the system from overloading. It downloads a variety of famous miners, such as Xmrig, t-rex, and NBMiner, and stores them in a folder named MicrosoftEdge under the base path.

• Keylogger Lib: api.js The keylogger stores its data in the kl folder located under the default path.

• File manipulation Lib: files.js This provides the functions for file uploading and exploring.

• Browser information stealer Lib: Browser.js Byakugan can steal information about cookies, credit cards, downloads, and auto-filled profiles. The data is stored in the bwdat folder under the base path. It can also inject cookies into a specified browser.

In addition, there are some features that help Byakugan live as long as possible:

• Anti-analysis If the file name is not chrome.exe or is not located in the ChromeApplication folder, it will pretend to be a memory manager and close itself.

In addition, it sets the path it uses to the Windows Defender’s exclusion path and allows files in the Windows firewall.

• Persistence It drops a configuration file for the task scheduler into the Defender folder under the base path, which makes it execute automatically when starting up.

There is a growing trend to use both clean and malicious components in malware, and Byakugan is no exception. This approach increases the amount of noise generated during analysis, making accurate detections more difficult. However, the downloaded files provided critical details about how Byakugan works, which helped us analyze the malicious modules. FortiGuard Labs will continue to monitor this malware and provide updates on this variant as they become available.

Fortinet Protections

The malware described in this report is detected and blocked by FortiGuard Antivirus as:

W64/BKGStealer.854C!tr W64/BKGStealer.4C6A!tr W64/BKGStealer.47AF!tr PDF/TrojanDownloader.Agent.BKN!tr

FortiGate, FortiMail, FortiClient, and FortiEDR support the FortiGuard AntiVirus service. The FortiGuard AntiVirus engine is part of each of these solutions. As a result, customers who have these products with up-to-date protections are protected.

The FortiGuard CDR (content disarm and reconstruction) service can disarm the malicious macros in the document.

We also suggest that organizations go through Fortinet’s free NSE training module: NSE 1 – Information Security Awareness. This module is designed to help end users learn how to identify and protect themselves from phishing attacks.

FortiGuard IP Reputation and Anti-Botnet Security Service proactively block these attacks by aggregating malicious source IP data from the Fortinet distributed network of threat sensors, CERTs, MITRE, cooperative competitors, and other global sources that collaborate to provide up-to-date threat intelligence about hostile sources.

If you believe this or any other cybersecurity threat has impacted your organization, please contact our Global FortiGuard Incident Response Team.

Git repository

github[.]com/thomasdev33k github[.]com/fefifojs github[.]com/wonderreader

blamefade.com[.]br thinkforce.com[.]br

c7dbb5e9e65a221a5f78328b5a6141dd46a0459b88248e84de345b2a6e52b1d9 c6fe9169764301cadccb252fbed218a1a997922f0df31d3e813b4fe2a3e6326d c9a27dbae96afb7d083577d30b2947c8ba9d1a6cb7e10e5f259f0929ef107882

9ef9bbfce214ee10a2e563e56fb6486161c2a623cd91bb5be055f5745edd6479 4d8eac070b6b95f61055b96fb6567a477dbc335ef163c10514c864d9913d23cb 30991c9cac5f4c5c4f382f89055c3b5e9bb373c98ce6a5516d06db3f8a478554

malware analysis , phishing

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Phishing campaign delivering three fileless malware: avemariarat / bitrat / pandorahvnc – part ii, new variant of qakbot being spread by html file attached to phishing emails, delivery of malware: a look at phishing campaigns in q3 2022, news & articles.

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Double layer security using crypto-stego techniques: a comprehensive review

• Review Paper
• Published: 13 October 2021
• Volume 12 , pages 9–31, ( 2022 )

Cite this article

• Aiman Jan 1 ,
• Shabir A. Parah   ORCID: orcid.org/0000-0001-5983-0912 1 ,
• Muzamil Hussan 1 &
• Bilal A. Malik 2  

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Recent advancement in the digital technology and internet has facilitated usage of multimedia objects for data communication. However, interchanging information through the internet raises several security concerns and needs to be addressed. Image steganography has gained huge attention from researchers for data security. Image steganography secures the data by imperceptibly embedding data bits into image pixels with a lesser probability of detection. Additionally, the encryption of data before embedding provides double-layer protection from the potential eavesdropper. Several steganography and cryptographic approaches have been developed so far to ensure data safety during transmission over a network. The purpose of this work is to succinctly review recent progress in the area of information security utilizing combination of cryptography and steganography (crypto-stego) methods for ensuring double layer security for covert communication. The paper highlights the pros and cons of the existing image steganography techniques and crypto-stego methods. Further, a detailed description of commonly using evaluations parameters for both steganography and cryptography, are given in this paper. Overall, this work is an attempt to create a better understanding of image steganography and its coupling with the encryption methods for developing state of art double layer security crypto-stego systems.

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Coalescing Image Steganography and Cryptography for Information Security

A Study on Integrating Crypto-Stego Techniques to Minimize the Distortion

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1 Introduction

The increasing bandwidth and data rates of 4G/5G cellular technology and optical fiber communication revolutionized data communication. The exchange of data in form of text, images, audio, and video over the internet has become ubiquitous [ 1 ]. The multimedia data is being exchanged by governments, law enforcement agencies, and for telemedicine by the hospitals [ 2 ]. During the covid-19 worldwide lockdown traffic of information over the internet saw an upsurge. Although internet usage has various advantages, but security and privacy of data still remain a challenge [ 3 ]. Data thefts, modifications, and alterations have become possible due to the availability of various tools in the hands of hackers. Thus, the security of data has become a challenging task of paramount importance for researchers [ 4 ]. Various information protecting mechanisms like, cryptography and data hiding methods as shown in Fig.  1 , have been put forward to address data security issues [ 5 ]. Cryptography scrambles and converts the secret data into an unreadable form for an unauthorized person [ 6 , 7 ]. Cryptography can be performed either with standard encryption techniques or Chaos based encryption techniques [ 8 ]. There are various standard encryption techniques (SET), like Data Encryption Standard (DES), Advanced Encryption Standard (AES), and Rivest Shamir Adleman algorithm (RSA), etc. [ 9 ]. In these approaches, the secret key is used to encrypt the important data before embedding [ 10 ]. However, the bulk of data with key lengths is the main limitation of the SETs which makes them insecure and less reliable for data encryption [ 11 ]. The limitation of SETs has been overcome by the chaos based encryption approaches. Chaos encryption approach uses initial keys for encryption that are sensitive to the changes made [ 12 ]. Thus, the chaos-based encryption approaches are more secure cryptographic method to ensure security to the data [ 13 ]. Cryptography can provide the considerable security to the data by changes the form of the original data through encryption. But, cryptography alone cannot resist security attacks, as its encrypted form attract attacker’s attention and hence can be modified or hacked. However, it is an insufficient method to provide data security as its encrypted form may draw the attention of an eavesdropper [ 14 ]. Hence, data hiding has been used widely by researchers to hide the existence of important data without being noticed by an intruder [ 15 , 16 , 17 ].

Different Information Protecting Mechanisms

Data hiding can be divided into two methods: steganography and watermarking [ 18 ]. Watermarking is the method of verifying the authenticity of multimedia (image, video, or audio) for copyright protection [ 19 ]. It can be either visible or invisible watermarking to authenticate the ownership. Steganography is the art of hiding important data into any multimedia for secret communication [ 20 ]. It is an invisible process in which detection of data is not easy. Steganography can be separated into two categories: Technical and Linguistic steganography approaches [ 21 ]. Technical steganography can be further subdivided into two sections based on the multimedia used and the technique applied. Steganography technique has various advantages associated with it, the only difficulty of it is to maintain the image quality with a good amount of payload [ 22 ]. Huge secret data size degrades the quality of an image that can suspect the presence of data [ 23 ]. And, if the eavesdropper detects the steganographic method, it can easily recover the data. So, with the concealing approach, data should be preprocessed by including some cryptographic approaches [ 24 ]. Hence, the present trend is of crypto-stego mechanism in which along with steganography, cryptography method is included to provide double layer security to the data as shown in Fig.  2 .

General Steganography approach together with Cryptography (Crypto-Stego)

PSNR value of existing method for 0–2 bpp embedding capacity

PSNR value of exisitng method for 2–6 bpp embedding capacity

PSNR value of exisitng methods for 12–24 bpp embedding capacity

In this paper, the most recent literature in the area of steganography and crypto-stego has been reviewed to facilitate an exhaustive reference for further research in this area. This paper further discusses the various state-of-art techniques, their limitations, and challenges. The analysis parameters have been also discussed. The main contributions of this paper is:

A comprehensive review of various image crypto-stego methods has been presented.

Various parameters for evaluation of crypto-stego schemes have been presented.

A deep insight into double layer schemes and future research directions have been presented.

The rest of the paper is organized as follows. Section  2 includes the literature survey. Evaluation parameters are discussed in Sect.  3 . Section  4 comprises the directions and future research directions. The paper concludes in Sect.  5 .

2 Literature survey

Steganography is the art of concealing data into any multimedia for covert communication [ 25 ]. Steganography can be divided into many types based on the cover object used to attain security, like text, image, audio, video, and network [ 26 , 27 , 28 , 29 , 30 , 31 ]. Further, the steganography can be divided into two domains based on the techniques used to hide data in any multimedia, i.e., spatial domain technique and frequency domain technique [ 32 ]. Special domain can be further subdivided into various versions, like least significant bit (LSB), pixel value differencing (PVD), pixel indicator technique (PIT), Edge based technique [ 33 ]. Frequency domain can be also subdivided into different types, like discrete fourier transform (DFT), discrete cosine transform (DCT), discrete wavelet transform (DWT) [ 34 ]. In this section, the existing literature on different steganographic techniques and crypto-stego has been discussed. The paper focuses on an analysis of publications in reputed journals from 2016–2021 to be taken into consideration for future work. The various steganography data hiding technique and its coupling with cryptographic approaches have been discussed in the subsequent sections. Incorporating cryptography with the steganographic approach provides better security to the data, therefore using the steganography technique alone is ‘less secure’ compare to the combined approach. As once the steganographic pattern gets revealed to the unintended user, the secret data is prone to hacks and modifications.

2.1 Spatial domain technique

A detailed description of the spatial domain based image steganography techniques has been presented. Table 1 highlights the pros and cons of the reviewed spatial domain techniques.

In 2021, AbdelRaouf [ 35 ] has proposed a novel image steganographic data hiding method. The method has used the adaptive least significant bit (LSB) method for hiding information into every color channel of an image. The method has a huge payload but takes more time for embedding data into an image. Although the technique shows considerable image quality values, however, the proposed method can be improved by introducing an encryption method before embedding to improve security. In 2021, Hussain et al. [ 36 ] have put forth an enhanced adaptive data hiding approach in which the least significant bit (LSB) and pixel value differencing (PVD) methods have been used for increasing hiding space. The method has good imperceptibility and embedding capacity. The proposed approach has focused only on payload and imperceptivity. The technique can be improved in the security domain by incorporating an encryption algorithm. In 2020, Sahu and Swain [ 37 ] have suggested Dual-layer based reversible image steganography using modified least significant bit (LSB) matching. In this approach double layer embedding has been performed to improve image quality. In the first layer, two bits of the secret data are embedded into each pixel using modified LSB matching to develop intermediate pixel pair (IPP). Whereas, in the second layer, four bits of secret information are embedded using IPP. The technique is good enough to provide a good payload with better image imperceptivity. The proposed approach can be enriched by introducing security algorithms to enhance the security of the data embedded in an image. Also, the proposed method can be extended to color images and can be improved to reduce processing time. In 2017, Hussain et al. [ 38 ] have presented a data hiding method to achieve high payload, good visual quality, and maintain security. This method is based on two novel approaches: parity-bit pixel value difference (PBPVT) and improved rightmost digit replacement (iRMDR). In this method, the cover image is separated into two non-overlapping pixel blocks. The embedding algorithm PBPVD (higher level ranges) or iRMDR (lower level ranges) are selected according to the difference value between two pixels in each block. The PBPVD technique increases embedding capacity and iRMDR attains good visual imperceptivity. However, the method can be improved by establishing a security related algorithm to improve the reliability of the method against attacks. Further, the scheme can be extended for color images also.

In 2018, Malik et al. [ 39 ] have come-up with a new data hiding approach using absolute moment block truncation coding (AMBTC) compression. In this method, AMBTC compresses the cover image to embed important data into the compressed pixel values. The method is able to maintain image quality with a good payload. However, security can be improved by incorporating cryptographic approaches. In 2019, Kumar et al. [ 40 ] have proposed an enhanced method of data hiding using absolute moment block truncation coding (AMBTC) using pixel value differencing (PVD) and hamming distance. The cover image is compressed with AMBTC and is divided into three sections: smooth, less-complex, and highly-complex blocks. One bit per pixel is embedded into smooth blocks, 8-bits are embedded into a less-complex block using hamming distance calculation, and more data bits are embedded into a highly-complex block using the PVD technique. The method produces a good embedding space. However, can be extended in the security domain to further increase security to the data.

In 2020, Chakraborty and Jalal [ 41 ] have put forth a novel image hiding method using local binary pattern (LBP). In this scheme, the cover image is separated into 3 × 3 non-overlapping blocks. The reference pixel of each block is encoded using LBP. The encoded image is XORed with the secret image. The image has been shuffled further to embed secret image bits in the LSB of the cover image. The method has strong statistical features against attacks. The presented method can be further extended to embed data by applying payload-specific hand-crafted descriptors. In 2020, Lin et al. [ 42 ] have proposed a new image hiding method for Dynamic GIF (Graphics Interchange Format) images. The presented scheme is based on the Palette sort. In this method, the cover image is decomposed into frames (static GIF image). A new distortion function is applied to all frames. The data is then embedded into these frames to form the stego frames. In this technique, the data is secured by hiding it into an image but with less payload. In 2018, Parah et al. [ 43 ] have presented computationally efficient and reversible data hiding method for secure electronic health record (EHR) communication. In this method, modular arithmetic has been used for embedding EHR into a medical image. Prior to embedding, the cover medical image is scaled up using the pixel reputation method. The proposed scheme has improved image quality with a better amount of payload but takes a good amount of time for embedding data into an image. The security of the method can be improved by implementing some encryption method. The technique can also be extended to color images to increase EHR embedding capacity.

In 2021, Li et al. [ 44 ] have suggested a high capacity image steganography technique. The method has used retracing extended Sudoku (RE-Sudoku) reference matrix for data embedding. The paper has an enhanced payload with good image quality. The method can be improved in security domain to enhance its superiority. Also, the method can be analyzed on color images to show its generality. In 2020, Saha et al. [ 45 ] have come up with a new image hiding method using a hashed-weightage Array. The method is based on extended exploiting modification direction (EMD) that creates less distortion in an image. The presented method has improved payload and reduces distortion of an image than the existing method. The security of proposed scheme can be enhanced by encrypting the secret data. In 2018, Shen et al. [ 46 ] have proposed a new data hiding method based on improved exploiting modification direction (EMD) and interpolation techniques with consideration of human visual system (HVS). In this scheme, the cover image is separated into various 3 × 3 non-overlapping blocks. The presented method hides data into each block by calculating the neighbor mean value and the difference value. The discussed method has improved embedding efficiency and quality than the traditional EMD technique. The presented method takes more time for processing the task. The scheme can be enriched by applying stegna-analysis resist method to improve security. Moreover, the technique can be improved by applying it to color images as well.

In 2016, Bairagi et al. [ 47 ] have put forth an efficient image steganography method for secure communication in the Internet of Things (IoT) infrastructure. This article has presented three information hiding approaches using RGB image steganography. In this method, data has been embedded in an image using a secret key and carrier information. The technique has improved the security of the data than the traditional steganographic techniques. This work can be enhanced by applying encryption approaches for security improvement. In 2020, Rim et al. [ 48 ] have presented a Beta chaotic map based image hiding method. The method used a Beta chaotic map to sort the embedding positions of an image. The simple least significant bit (LSB) technique has been used to embed data into an image. The technique has provided good security to the data than the traditional steganographic approaches. However, the method can be improved to gain more capacity with good image quality. In 2021, Hassan and Gutub [ 49 ] have introduced an efficient image reversible data hiding (RDH) approach. In this method, interpolation optimization has been used to scale up the original image before embedding. The advantage of this method is a good image quality with a huge payload. However, the cryptographic technique can be applied to the secret data before embedding to enhance security.

2.2 Edge based technique

This subsection discusses different edge-based image steganography methods. Table 2 highlights the pros and cons of these techniques after analyzing the surveyed papers.

In 2021, Atta and Ghanbari [ 50 ] have put forth a new data hiding mechanism based on dual tree complex wavelet transform (DT-CWT). In this method, the cover image has been divided into subbands and neutrosophic edge detector (NSED) has been applied on these subbands to attain edge areas. The secret data is embedded into the detected subband edge regions of an image. The method has been able to get good imperceptivity with a good amount of payload. However, needs to secure data by applying some encryption algorithms. In 2019, Dhargupta et al. [ 51 ] have proposed an image steganography approach using Fuzzy edge detection (FED). In this approach, edges of the cover image are detected by FED to embed data into edge areas of an image. Data to be embedded in an edge region depends upon the Euclidean distance and is determined by the Gaussian function. The technique shows good embedding capacity, but with an increase in payload, its quality gets degraded. The method has been applied only on grayscale images. In 2019, Ahmadian and Amirmazlaghani [ 52 ] have suggested a secret image sharing steganography scheme. This method is dependent on fully exploiting modification direction (FEMD) and edge detection technique to embed data into the cover image. The method has been able to improve image quality than the existing methods, but, has less embedding space. In 2018, Kich et al. [ 53 ] have offered an edge detection method based image steganography method. In this approach, secret information has been dissimulated into edge pixels of an image using modified simple linear iterative clustering (M-SLIC) algorithm. The technique has a good payload, robustness, and imperceptivity. However, the scheme can be also upgraded in the security domain. In 2018, He et al. [ 54 ] have suggested a reversible information hiding method using edge information. The method has been proposed for high dynamic range (HDR) color images. The data has been embedded into HDR color images by using edge information. The approach has achieved less visual distortion after embedding secret data into an image. However, the security of the data by preprocessing has not been considered to increase data security. In 2018, Gaurav and Ghanekar [ 55 ] have introduced an edge region detection based image steganography technique. The secret data has been embedded into the least significant bit (LSB) bits of edge pixels. The approach has good image quality with a better payload. However, can be improved to insert a cryptographic approach along with a steganographic method to ensure better security to the data. In 2020, Prasad and Pal [ 56 ] have introduced a new image steganography algorithm, using edge detector and modulus function. For embedding, the modulus function has been used, whereas, edge detector has been used to find edge locations to embed data into edge areas. The method has been tested only on greyscale images. Further, the technique can be improved by introducing an encryption algorithm to increase security.

In 2019, Mukherjee and Sanyal [ 57 ] have presented an edge based image steganography method with a variable threshold. The edges of an image have been detected by the Sobel edge detector with respect to the threshold key. These detected regions are used to hide secret information. The scheme has gained good image quality with considerable hidden data capacity. The suggested method can be further improved by incorporating encryption schemes to the embedding data before hiding. In 2018, Kadhim et al. [ 58 ] have put forth an adaptive image steganography technique. The technique is based on edge detection over dual-tree complex wavelet transform (DT-CWT). Edge regions of an image have been detected using the canny edge detection method. Even though the method has good embedding capacity and image quality, however, can be extended in the security domain to increase data security against attacks. In 2018, Ghosal et al. [ 59 ] have put forth an image steganography technique based on the Laplacian of Gaussian (LoG) edge detector. In this approach, the data has been embedded into all non-edge pair, edge pair and mixed pair of an image. By this technique, the burden of payload into the traditional method of embedding all data into edge areas only has been reduced. The method no doubt has increased payload and image quality. However, the technique can be further enhanced in the security domain. In 2021, Ghosal et al. [ 60 ] have suggested an image hiding approach based on the Kirsch edge detector. In this method, the data has been embedded into both edge and non-edge regions of an image in such a way that image distortion is minimized. The method has improved payload with less image distortion. The proposed method can be improved by applying a cryptographic method to enhance security. In 2019, Banik et al. [ 61 ] have discussed an image steganography method for embedding capacity improvement. The technique has used Kirsch detector to find the edge regions of an image for data hiding. The threshold value has been used as a key to embed data into edge areas of an image. The technique has good image quality with a good payload. The algorithm has been implemented only on greyscale images. Thus, the method can be extended to color images as well. In 2020, Wang et al. [ 62 ] have presented a high capacity hybrid steganography method based on the least significant bit (LSB) and hamming code (HLAH). In this technique, the color cover image is separated into 9 × 9 blocks and the red (R) plane is extracted. The LSB of the R plane is set to ‘0’ and then the canny edge detection technique is used to find the edges of a plane. The method has used the canny edge detection technique to find edges. The data is embedded into the non-edge and edge regions using LSB and (3,1) hamming code. The method has been able to provide a good payload with better image quality than the existing methods. However, the security of the algorithm can be improved by encrypting the secret data prior to embedding.

In 2020. Tripathy and Srivastava [ 63 ] have proposed an edge based data hiding technique using Modulus-3 strategy and comparative analysis. In this method, the data has been changed into ternary data and has been embedded into edge areas of an image using the Modulus-3 strategy. The edge regions have been detected with Sobel, Prewitt, Canny, and Laplacian edge detectors. The method has been able to hide a vast amount of data into an image with good visual image quality. However, the security of the secret data can be further improved by introducing an encryption method along with the proposed algorithm. In 2019, Tuncer and Sonmez [ 64 ] have introduced the image steganography algorithms based on edge detectors and a 2  k correction scheme. The method has used Sobel, Canny, Laplacian of Gaussian (LoG), block based edge detection (BED), and hybrid edge detectors (HED) to find edge pixels of an image. These detected regions have been used to embed data into it using the least significant method (LSB) and 2  k correction method. The technique has the good capacity with good visual quality, but, has not improved much upon security parameter.

2.3 Frequency domain technique

A thorough summary of the techniques reviewed under the frequency domain is given as follows and their pros and cons are pointed out in Table 3 .

In 2021, Abdel-Aziz et al. [ 65 ] have offered an improved data hiding method for securing color images. In this work, the authors have used a hyper chaotic map and left-most significant bit (LMSB) embedding method to embed data into an image. In this approach, the cover image has been first encrypted using a hyper chaotic map to form the encrypted image and is then converted into the YCbCr channels. Of them, the first channel (Y) has been divided into non-overlapping blocks using the DCT technique. The resulted blocks are quantized and secret data has been embedded into frequency coefficients of these quantized discrete cosine transform (DCT) blocks using Huffman coding. The output stego image is further XORed with the remaining two channels (CbCr) to form the encrypted stego image. The scheme has good security and image quality. However, an encrypted form of stego image can suspect the presence of data. Also, the technique can be extended for embedding any type of data into color images. In 2021, Yao et al. [ 66 ] have presented a reversible data hiding (RDH) approach for dual JPEG images. In this method, the cover image is preprocessed to attain DCT coefficients and to ascribe embedding space. Then, secret data has been embedded into obtained frequency coefficients of image pixels using the dual-JPEG RDH scheme. The technique has gained a considerable embedding space with good visual image quality. However, the secret data to be embedded has not been preprocessed to improve data security. In 2018, Liu and Chang [ 67 ] have suggested a reversible data hiding (RDH) technique for JPEG images for hiding a great amount of data. In this approach, the discrete cosine transform (DCT) technique has been used to gain the coefficient values for non-overlapping 8 × 8 blocks. The quantization method has been applied to each block of an image to attain JPEG quantized block. Then, the AC coefficients are scanned in a zigzag direction to embed secret data into non-zero elements. The proposed method has been able to gain good image quality with considerable payload, but, the enhancement of security to the data with preprocessing has not been taken into consideration. In 2018, Attaby et al. [ 68 ] have put forth a novel data hiding approach using discrete cosine transform (DCT) to embed data into JPEG images. The difference value of two DCT coefficients using modulus 3 has been applied to insert two secret data bits into the values of the coefficients of an image. This significantly reduces the image distortion with improvement in image embedding space. However, the data security of the scheme can be improved by applying a cryptography approach to secret information before embedding. In 2016, El-Rahman [ 69 ] has given a new steganographic tool to hide data into the frequency domain of an image using the discrete cosine transform (DCT) method. The technique has hidden the important information about nuclear reactor in the middle frequency. The scheme is able to protect image quality with a high considerable amount of embedding capacity. However, the confidential information of the nuclear reactor needs to be more secure from any thefts. Therefore, encryption of important information before embedding could be more effective for data security. In 2020, Mohamed et al. [ 70 ] have presented an L*a*b* (luminance channel ‘L*’ and chrominance channels ‘a*and b*’) color space image steganography technique using quad-trees. The method has applied the quad-tree of the gray-scaled cover to the RGB of the cover image. The resultant blocks of the quad-tree are transformed to L*a*b* color space using 2dimentional-discrete cosine transform (2D-DCT) to embed data into the largest zero areas of DCT. The method has considerable embedding capacity, good image quality, and secure than the existing spatial and frequency domain steganography methods. However, the technique is complex. In 2018, Rabie et al. [ 71 ] have suggested increased embedding space and image quality data hiding scheme based on transform domain mechanism. The presented scheme uses the quad tree segmentation method to divide the blocks of the cover image. In each block, quantization step and piecewise linear curve fitting are used to hide secret data in the least significant areas of discrete cosine transform (DCT) coefficient areas. The method has tried to gain a better image payload. However, the data is embedded directly without pre-processing that may lead to security issues. In 2017, Saidi et al. [ 72 ] have put forth a new steganographic approach Based on discrete cosine transform (DCT) and chaotic map. The presented approach is used to find coefficients of the cover image. Piecewise linear chaotic map (PWLCM) has been used to scramble embedding positions to improve security to the embedding data. The algorithm has a good payload, however, the technique has poor image imperceptivity. Also, the technique can be improved further to resist any statistical attacks.

In 2018, Nipanikar et al. [ 73 ] have discussed a sparse representation based image steganography technique using particle swarm optimization (PSO) and discrete wavelet transform (DWT) approaches. These approaches have been used to locate the appropriate pixels for embedding speech signal in the cover image. The method is able to achieve good image imperceptibility and payload. However, the algorithm can be extended in the security domain to enhance data security. In 2018, Nevriyanto et al. [ 74 ] have presented an image steganography method using discrete wavelet transform (DWT) and singular value decomposition (SVD) techniques. In this scheme, a text file has been converted into an image to form a watermark for embedding it into an image. The secret watermark has been embedded into the frequency coefficients of an image using the SVD method. The method can be further made secure enough against attacks by incorporating encryption methods on the data and can be also extended to color images. In 2018, Miri and Faez [ 75 ] have introduced an image steganography technique for hiding important data into frequency coefficients of an image. This method has used the integer wavelet transform (IWT) method to obtain the frequency coefficients of an image. The method has been analyzed on greyscale images that can be extended to color images. The security of the data can be further improved by introducing encryption methods to the secret data before embedding. In 2019, Kalita et al. [ 76 ] have proposed a new steganography method using integer wavelet transform (IWT) and the least significant bit (LSB) substitution method. To estimate the embedding capacity, the coefficient value differencing method has been applied. The scheme has good visual quality of an image and considerable embedding capacity. However, the data can be secured from statistical attacks by introducing encryption algorithms. In 2021, Ghosal et al. [ 77 ] have presented exploiting Laguerre transform (LT) based image hiding method. In this scheme, the cover is separated into m-pixel non-overlapping groups. Each pixel of these groups is then transformed into its equivalent coefficients using LT. The secret data is embedded into these coefficients. The resulted pixels are adjected to minimize distortion and have been recomputed by applying inverse LT (ILT). The presented scheme has been able to increase payload with good image quality but takes more time for completing the process. However, the method can be secured by encrypting the data. In 2019, Ma et al. [ 78 ] have come up with a new reversible data hiding (RDH) approach for medical images. The method is based on block classification and code division multiplexing (CDM). In this scheme, the non-overlapping blocks of the medical image are categorized into smooth and texture groups by calculating mean square error (MSE). The secret information has been embedded into transformed frequency coefficients using the integer-to-integer discrete wavelet transform (IDWT) technique of the texture block. The method has large data hiding capacity with good image imperceptibly. Security to the secret data can be further improved by applying cryptography approaches to the secret data prior to embedding. In 2020, Murugan and Subramaniyam [ 79 ] have proposed an image steganography technique to ensure the data security transaction over an insecure network. The data has been embedded into coefficient values of an image using the alpha factor. The image pixels have been transformed with the 2dimentional-Haar discrete wavelet transform (2D-Haar DWT) to gain four sub-bands. The method has good embedding space while preserving image quality than the existing schemes. Also, the proposed method is secure than already existing frequency domain techniques. However, the proposed method is complex.

2.4 Joint Crypto-stego schemes

This subsection includes the reviewed papers of different dual security image steganography techniques. These techniques have encrypted the secret data/ secret image with either standard encryption method or chaotic method before embedding to provide double layer security to the secret data. The pros and cons of different dual security image steganography methods are shown in Table 4 . The following subsections discuss the different combined image steganography and encryption techniques.

Simple domain crypto-stego schemes

The review of different special domain based image steganography and encryption methods are explained below:

In 2021, Maji et al. [ 80 ] have presented a spatial domain based image steganography method in which higher order pixel bits have been used to embed data. To encrypt data, XOR operation has been used to enhance security. Since the method has been implemented on the greyscale image. Thus, the presented approach can be extended to color images to prove its generality. In 2017, Sharif et al. [ 81 ] have presented an image steganography technique based on a 3-dimensional chaotic map. In this method, the pixel position for embedding changes with the change in the cover image. Thus, makes the system secure against any statistical attacks. However, the method can be improved in terms of complexity. In 2020, Gambhir and Mandal [ 82 ] have proposed chaos based least significant bit (CLSB) steganography method. The technique has used logistic map chaotic function to develop random numbers. The scheme shows good security and image quality results. However, can be improved further to increase embedding space to hide huge data into an image. In 2019, Prasad and Pal [ 83 ] have proposed logistic map based image steganography. This method has used the least significant bit (LSB) and pixel value differencing (PVD) techniques to embed data into the cover image. The embedding data is encrypted using a logistic map to improve security to the data. The proposed algorithm has attained better security. The technique can be modified in a different way to achieve better visual image quality and can be extended to color images also. In 2017, Mohammad et al. [ 84 ] have offered image steganography for visual contents authenticity. The method uses color model transformation, three-level encryption algorithm (TLEA), Morton scanning (MS). The data has been embedded with the direct least significant bit (LSB) substitution method using MS. TLEA has been used to encrypt data. The scheme provides the best algorithm for visual content authenticity in social networks. While the technique is complex and has less payload. In 2019, Alotaibi et al. [ 85 ] have proposed a secure framework for safe data transmission in mobile devices using hash, cryptography, and steganography. The proposed method has used a hash function for storing the secret password, cryptography with AES method for encrypting password, and LSB method for hiding encrypted password into an image. The method is able to provide a considerable amount of payload but has low visual quality of an image. In 2021, Mathivanan and Balaji [ 86 ] have suggested a QR code based color image stego-crypto technique. The method uses dynamic bit replacement and logistic map for embedding and encryption respectively. In this approach, the secret data is converted into 2D binary using base64 encoding method and QR code generator and is then embedded into an image using dynamic bit replacement technique. The image is then scrambled using a logistic map. The method is secure against differential attacks but is able to hide a low amount of secret data. In 2021, Abdelwahab et al. [ 87 ] have put forth an efficient image steganography technique to hide data for safe data transmission. In this scheme, plain text is encrypted using the RSA method and is embedded into the YCbCr components of an image using the LSB approach. The resulted stego image is then compressed using Huffman coding, Run Length coding (RLC), or DWT to form compressed stego image. The method has good amount of payload with considerable image quality. However, the scheme is complex.

Edge based crypto-stego techniques

Few edge detections based image steganography techniques along with data encrypting approaches are discussed as under:

In 2021, Parah et al. [ 88 ] have presented efficient security, high payload, and reversible electronic health record (EHR) data hiding approach. In this method, the cover image is scaled up with the pixel reputation method (PRM) and then the boundary conditions are applied to the image. The resulted image is divided into 2 × 2 blocks to embed data first into counter diagonal pixels and then to the main diagonal pixel after checksum computation. The EHR is secured by adding a watermark and applying the RC4 encryption method. The encrypted data is divided into 3-bit chunks and has been then embedded into the pixels after applying the left data mapping (LDM) method. The scheme has increased security to the data and has authenticated its originality. However, with a high payload, the quality of an image can be enhanced by testing the method to color images also. In 2016, Parah et al. [ 89 ] have proposed a new information hiding technique based on a hybrid edge detection method. In this scheme, the color image is separated into three planes (red, green, blue). The data is embedded into edge regions and non-edge regions of green and blue planes by making use of a hybrid edge detector. Whereas, the red plane is used to hold the bit status of green and blue planes. In order to authenticate and detect tempering the data, a fragile watermark is embedded. Before embedding, the secret information is encrypted using the RC4 algorithm. The algorithm is able to increase capacity with good image quality. Further, the security of the method is provided to the data by encrypting it prior to embedding. However, the time complexity of the method can be reduced by reducing the data extraction time. In 2020, Delmi et al. [ 90 ] have discussed the image hiding approach using the edge adaptive method and chaos cryptography technique. In this technique, the data has been encrypted using the Arnold Cat Map function. Whereas, the data has been embedded into the edge region of an image located by a canny edge detector. The technique although has increased security to the data. However, can be enhanced to increase payload with good image quality. In 2021, Sharma et al. [ 91 ] have suggested crypto-stego approach to prevent data from attacks. In this method, Rabin cryptosystem is used to encrypt the secret data, Arnold transform has been used to scramble the data, and the resulted data has been embedded into the edges of multiple images. The framework is able to provide better image quality with a considerable payload. However, the proposed approach is complex.

Frequency domain crypto-stego techniques

Different frequency domain based image steganography together with data encryption surveyed methods have been discussed in this subsection:

In 2016, Kaushik and Sheokand [ 92 ] have put forth a steganography scheme based on chaotic least significant bit (LSB) and discrete wavelet (DWT) approaches. In this method, the data is encrypted using a logistic map. Whereas, the encrypted data is embedded into frequency coefficients of the cover image using DWT by 3–3-2 LSB insertion scheme. The security of the proposed method has been increased by using the chaotic method. The method has good image quality but has less embedding capacity. In 2020, Panday [ 93 ] has suggested a new medical image steganography approach using a bit mask oriented generic algorithm (BMOGA). BMOGA has been used together with cryptographic features to encrypt data. In this approach, data has been embedded in the frequency coefficients of an image. Discrete wavelet transform (DWT) (1-level & 2-level) has been used to find frequency coefficients. The technique is secure and imperceptible, however, can be enhanced to gain a good payload. However, the scheme can be further modified to reduce the complexity of the algorithm. In 2018, Elhoseny et al. [ 94 ] have presented a secure medical data transmission model for internet of things (IoT) based healthcare systems. The method has used 2-D discrete wavelet transform (2D-DWT) to conceal data into both grayscale and color images. The secret information has been encrypted using a hybrid encryption scheme using advanced encryption standard (AES) and Rivest, Shamir, and Adleman (RSA) algorithms. The technique shows good image quality with good data security. However, the technique is complex.

In 2020, Duan et al. [ 95 ] have proposed a high-capacity image hiding scheme. In this method, the data have used encrypted with elliptic curve cryptography (ECC) approach. Discrete cosine transform (DCT) is applied to the secret image prior to encryption. A deep neural network has been used to increase the payload of the cover image. The method has been applied to both gray images as well as a color images. The method shows better security to the data. However, can be improved to reduce algorithm complexity. In 2018, Subhedar and Mankar [ 96 ] have presented image steganography using curvelet transform (CT) to embed secret information in the selected cover image. In order to obtain curvelet coefficients, level 6 CT has been applied to the cover image. Encryption data has been embedded into appropriate blocks by using standard deviation. Whereas, image block has been replaced with secret data block using spread spectrum when the deviation is higher than a threshold. The resulted stego image is obtained by applying inverse CT. In this technique, security has been heightened by encrypting the data using Arnold transform. However, it can be enhanced in payload and quality domain by testing it on the color image also. Further, the method is complex. In 2020, Eyssa et al. [ 97 ] have introduced an efficient image steganography method. In this approach, the data has been encrypted with the Baker map together with a discrete cosine map (DCT). The secret message in the presented method has been embedded into the values of the coefficients of an image. The security of the system has been increased. However, with a good amount of payload the image quality is less that needs to be enhanced. In 2021, Kaur and Singh [ 98 ] have introduced a new data hiding method based on discrete cosine transform (DCT) and coupled chaotic map. The secret data in this approach has been encrypted using coupled chaotic map of logistic and sine map. In this approach, the data has been embedded into the DCT coefficients of an image. The technique has a good payload, but, has been applied only on greyscale images and is complex.

Medical image crypto-stego techniques

The review of various medical image steganography together with data encryption has been discussed in this subsection:

In 2020, Hureib and Gutub [ 99 ] have proposed a combined cryptography and image steganography approach to enhance security in medical health data. In this method, Elliptic curve cryptography has been used to encrypt text before hiding. Then, the encrypted text is embedded into image pixels to hide its presence from the person who is not authorized. The technique has a good payload and image quality, however, it has not been tested for various attacks, like statistical attacks, etc. In 2020, another image steganography technique using elliptic curve cryptography has been presented [ 100 ]. The method has used 1-LSB and 2-LSB image steganography techniques to hide data into an image. The medical data has been embedded in the medical image after encrypting it with cryptography technique to improve data security. The method provides good image quality but has low embedding capacity. In 2019, Samkari and Gutub [ 101 ] have suggested a protecting mechanism for medical records against cybercrimes within the Hajj period. The method has used a 3-layer security system for medical record protection by encrypting data using hybrid cryptography method and hiding encrypted data into an image to produce stego-image for sending through the internet. The presented approach has good image quality and is safe for data transmission, however, the method is complex. In 2021, Denis and Madhubala [ 102 ] have put forth hybrid data encryption and hiding model for medical data security over cloud-based healthcare systems. In this scheme, a hybrid encryption model using AES and RSA algorithms has been used to convert the original data into an encrypted form. Whereas, the 2D-DWT and Adaptive Genetic Algorithm (AGA) based optimal adjustment method to conceal encrypted medical data into an image for safe data transmission. The proposed technique provides good image quality and is secure. However, the approach is complex and has low hiding space. In 2021, Manikandan et al. [ 103 ] have proposed an image steganography technique to secure e-health. In this approach, the secret image is first encrypted with the XOR cipher encryption and is then embedded into the cove image using LSB method. The framework has considerable image quality but provides low embedding capacity. In 2021, Ogundokum et al. [ 104 ] have presented crypto-stego medical information securing model. This technique uses international data encryption algorithm (IDEA) and Matrix-XOR techniques for enhancing security to the internet of medical things (IoMT) for the medical records protection. In this method, secret data has been encrypted using IDEA and is then embedded into carrier image using XOR. The method provides good visual quality of an image and is secure for medical data transmission. However, space for transmitting data in a concealed manner should be increased. In 2021, Prasanalakshmi et al. [ 105 ] have recommended a secure clinical data transmission technique in the internet of things (IoT) using hyperelliptic curve (HECC) and cryptography techniques. This approach has used AES and Blowfish hybrid cryptography for medical data encryption and HECC for embedding encrypted medical data into a medical image. The generated embedded image is then compressed with the 5-level DWT to attain a good payload and better security. The technique provides good image quality with considerable payload, however, the technique is complex.

From the literature, it has been observed that the use of cryptography and steganography together is able to provide double layer security to the data. Cryptography protects data from hacks, and in particular chaos based cryptography can be the better option for encryption because of its ergodicity and dependence on initial condition properties. And, incorporating a steganography approach with cryptography improves data security by concealing it into digital media. Data hiding using chaotic maps to locate and create a random sequence of pixels enhances data security. Also, concealing secret information in edge regions improves payload, can prevent cover image from noticeable distortion, and thus prevents attacks. Moreover, it has been observed that cryptography and steganography have widened their applications almost in all spheres of life, like in e-health, IoT, mobile devices, system login security, cloud, etc.

3 Evaluation parameters

Evaluation parameters are the tools used to check the efficiency, validity, and superiority of an algorithm. Outcomes of the analyzed scheme with various methods whose values lie between expected ranges prove the strength of the technique. The most common characteristics of image steganography and data encryption analysis are explained below in subsections.

3.1 Visual quality analysis

Steganography method alters the image by storing secret information inside pixels that affects the visual quality of an image. However, changes incorporated in an image should not be perceived by the invaders. There are various standard evaluating methods used to confirm the image quality strength, like, mean square error (MSE), peak signal to noise ratio (PSNR), structural similarity index (SSIM), etc. Table 5 summarizes the MSE, PSNR, NCC, NAE, and SSIM values achieved by different image steganography techniques in this study.

Mean square error (MSE):

MSE is the error measurement of the stego image with respect to its original image. It helps in comparing the difference between the pixel values of an original image and stego image. Lesser MSE value provides better quality of an image [ 106 ]. Therefore, the MSE value should be near ‘0’. MSE can be calculated as follows:

where p i and p i ’ respectively represent the pixel values of the original and stego image. And, n represents the image size.

Peak signal to noise ratio (PSNR):

PSNR is the visual quality measuring tool that measures the image alteration in the stego image compared to its original image. It estimates the difference in pixel values of a stego image in relation to its original image. For better image quality, the PSNR value should be more than 39dB [ 107 ]. PSNR values of different existing methods for different payloads are shown in Graphs 1 , 2 , and 3 below. PSNR can be measured as follows:

where MSE can be measured with Eq.  1 .

Normalized cross correlation (NCC):

NCC is the relation determination method of an image. It investigates the relationship between the original image and its corresponding stego image. A better correlation between images indicates better image visual quality. Hence, NCC near ‘1’ is considered as the good steganography algorithm [ 108 ]. NCC can be analyzed as follows:

where O and S respectively denote the original and stego image pixel values.

Normalized absolute error (NAE):

NAE calculates the error between the original image and stego image to measure image quality after embedding. Error between the images should be less (near ‘0’) to retain the image quality [ 88 ]. NAE can be expressed as follows:

where O is the original image pixel value and S is the stego image pixel value.

Structural similarity index (SSIM):

SSIM is the visual quality measuring tool to checks the similarity between the original and stego image. The value of SSIM close to ‘1’ (means 100%) represents the good image quality [ 109 ]. SSIM can be mathematically represented as follows:

where µ i and µ j are the mean intensity, σ i and σ j are the standard deviations, and σ ij is the cross-covariance of images i and j respectively.

3.2 Embedding capacity analysis

Embedding capacity (EC) is another steganographic analysis parameter that calculates the number of secret data bits embedded into a per pixel of an image. The bits per pixel (bpp) should be more while preserving image imperceptivity. EC can be examined as follows:

Here, size of an image is M × N × D, where M and N represents row and column of an image, and D represents the image pixel depth that can be either 1 or 3 depends upon the image selection (gray or color image). Therefore, maximum bpp for a grayscale image will be 8 and for color image it will be 24 (3 × 8 = 24). Embedding capacity for different image steganography techniques is shown in Table 5 .

3.3 Differential analysis

The strength of an algorithm is tested by checking the sensitivity of an encrypted image with its original image and secret key. The strength of an encryption algorithm should be strong enough to resist attacks. The well-known differential analysis is a number of pixel change rate (NPCR) and unified average change intensity (UACI).

Number of pixel change rate (NPCR):

NPCR is the security evaluating method that analysis the single pixel changes in the original image. It checks the sensitivity of the encrypted image compared to its original image and secret key. The acceptable NPCR value is 99.61% and closer to 100% is considered as the best encryption algorithm [ 110 ]. NPCR can be analyzed by the equation as follows:

where m and n are the image measurements. E T is the total number of unequal units. And, I o and I e are the original image and encrypted image respectively.

Unified average change intensity (UACI):

UACI is the other encrypted image strength checking method. It calculates the average intensity of the difference between the original image and encrypted image. More percentage difference in intensities ensures a good UACI value and the lowest acceptable UACI value is 33.44% [ 110 ]. UACI can be calculated by the equation as follows:

where I o and I e are respectively the original and encrypted images. And, m and n are the image measurements.

3.4 Entropy analysis

Entropy is the average information per bit existing in an image. It checks the pixel randomness of an encrypted image. The average range of entropy is between 0 to 8 and close to 8 for 8 bit is considered as the best entropy value [ 111 ]. Entropy can be computed as follows:

where S represents symbol collections, c i ϵ S , P(c i ) represents probability and n is the symbol number.

3.5 Key analysis

The key analysis is the main tool of an encryption algorithm for checking algorithm strength. The algorithm strength depends on key analysis. It can be checked in two ways: key space and key sensitivity analysis. Key space checks the size of the secret key used for encrypting an image. The larger size of the secret key makes the system secure against attacks, as it will be difficult for the unauthorized user to get the exact same key. Key sensitivity checks the sensitivity of the secret key to the changes made. Even a single bit change in the original key results in an altogether different image or unrecoverable image.

3.6 Statistical analysis

Statistical analysis checks the robustness of an encrypted image against attacks. The commonly used statistical analyses are histogram analysis and correlation coefficient for testing its robustness against attacks.

Histogram analysis represents the image by the number of data points within a definite range. The histogram gives the pixel distribution or uncommon forms of an image. Histogram analysis can be checked on stego image or encrypted image in comparison to the original image. In the steganography approach, the histogram deviations of the stego image with respect to its original image can suspect the presence of data in an image. Therefore, for histogram comparison of stego image, the stego image histogram should resemble the original image histogram to avoid statistical attacks. Whereas, the histogram of an encrypted image should be uniform to show its random behavior. Histogram uniformity among pixels claims the robustness of the encryption method. Thus, the histogram of an encrypted image should be uniform to prevent the data from statistical attacks.

Correlation coefficient (CC):

The correlation coefficient (CC) checks the relationship between the original image and the encrypted image. In the original image, the pixels are interconnected with each other in three different directions: horizontal, vertical, and diagonal. However, for encrypted images, the correlation range between the pixels is [1, 1] and near 0 (lesser correlated) is considered as the good encryption technique [ 112 ]. The correlation coefficient can be expressed as follows:

Here, i and j represent image coordinates and C(i, j) represents the covariance between them. σ(i) and σ(j) are the standard deviations of their respective image coordinates. m is the pixel pairs ( i n, j n ) number. And, x(i) and x(j) are the mean deviations of i n and j n respectively.

3.7 Randomness analysis

Randomness analysis checks the pixel haphazardness of an encrypted image. National Institute of Standards and Technology (NIST) [ 113 ] has developed a statistical test suite to find out the algorithm strength by analyzing several tests on an encrypted image. The NIST statistical test suite consists of almost 15 calculable tests. These tests include frequency test, frequency within block test, run test, longest run of ones in a block test, binary matrix rank test, discrete fourier transform (DCT) test, non-overlapping template matching test, overlapping template matching test, etc. This test is helpful in identifying deviations of a binary sequence of an encrypted image.

3.8 Speed analysis

Scheme efficacy is analyzed by the execution time of an algorithm to complete its task [ 114 ]. The execution time of a method depends upon the system configuration and the algorithm complexity. This analysis can be examined for different processing steps of a program, like, embedding process, encryption process, the extraction process, decryption process, etc. The execution time for any of the program steps should be as small as possible to make the method computationally efficient. The embedding and extraction of some image steganography techniques are shown in Table 6 .

3.9 Steganography effect on medical image accuracy analysis

Medical image accuracy analysis checks the accuracy and precision of resulted medical image after treating with the steganography method. The commonly used methods are accuracy, specificity, sensitivity, and precision, [ 115 ] for analyzing the performance and quality contents of an image after steganography.

Accuracy is the medical image quality content and prediction accuracy measuring tool. It is the ratio between all correct predictions (positive or negative) and the entire test set [ 115 , 116 ]. Accuracy can be measured by the Equation as follows:

where TP is true positive, TN is true negative, FP is false positive, and FN is false negative.

Specificity:

Specificity is also called as true negative rate (TNR). It is the ratio between the number of correctly predicted normal people and the total number of normal people [ 115 ]. Specificity can be calculated by the Equation as follows:

where TN is true negative and FP is false positive.

Sensitivity:

Sensitivity is also called true positive rate (TPR) and recall. It is the ratio between the correctly predicted people with a disease and the total number of actual diseased people [ 115 ]. Sensitivity can be computed as follows:

where TP is true positive and FN is false negative.

Precision is also called a positive prediction value (PPV). It is the ratio between the total number of positive predictions people with a disease and the total number of positive predicted people [ 115 ]. Precision can be expressed as follows:

where TP is true positive and FP is false positive.

4 Discussions and future research directions

For secure information transmission, the image steganographic technique is used to restrict intrusion. It embeds the secret data bits into the image pixels to hide the data from unauthorized extraction. Numerous image steganography techniques have been developed by researchers to ensure data security. New trends in image steganography are discussed in this comprehensive review. From the literature, it can be observed that most of the techniques have been able to provide security and safety to the data. The security of data has been enhanced by some of the crypto-stego techniques discussed to provide double layer security to the data. From the survey of the existing image steganography and crypto-stego techniques, it can be noticed that each algorithm has its advantages and disadvantages. A few of the important issues as future directions are highlighted below:

Using smooth regions for hiding data creates more distortion in an image and thus suspects the presence of data. Hiding data into the edge regions not only provides good image quality but increases the embedding capacity of an image. Therefore, edge based image steganography technique could be used to increase the hiding space and visual quality of an image.

Most of the image steganography methods discussed have taken grayscale images as a cover medium. Therefore, there is a need for developing and applying steganography methods also on color images to prove its versatility and to further increase payload.

Hiding data into the frequency coefficients of an image provides security to the data but provides less capacity and is a complex method. Therefore, robust algorithms should be developed in spatial domain to improve data security with less complexity for which the encryption of data together with steganography can be helpful.

Many of the existing image steganography approaches have tried to either improve payload, image quality, or security of the algorithm. Therefore, a good tradeoff between these parameters should be created to improve the overall performance of the method in all dimensions. For this, edge based image steganography and chaos based encryption methods could be used together. Although, the research in this field has been done by the researchers. Though, it is still an underdeveloped field that needs to be explored in various image steganography schemes and different chaotic maps.

A survey of the literature leads us to conclude that computational complexity is still an open area of research in the area of the image steganography method.

Experimental analysis of different reviewed techniques has not used all the evaluation parameters for analyzing the scheme. It can be observed from the survey that image steganography algorithms that encrypt data before embedding have not tested the technique for basic NPCR, UACI, and other security tools. To ensure strong security to the encrypted data. Therefore, every encryption method should be tested for different security tools.

The steganography approach should resist geometrical attacks, signal processing attacks, and noise attacks to ensure its reliability, so development of robust steganographic systems is an open area of research.

Several methods have been tested only on few image formats. Therefore, an effort should be done to create a benchmark dataset to compile the algorithm for different image formats.

5 Conclusion

Technological advancement is increasing exponentially to ease daily activities and for a fast service delivery system. For todays’ efficient communication system, the internet acts as a central pillar and reduces overconsumption of resources like time and cost. However, the internet is an open environment for data thefts, data modifications, etc. Therefore, to protect data/information from getting into the wrong hands, the image steganography method is used for ensuring data security. The purpose of this review paper is to present a comprehensive review of various image steganography and crypto-stego techniques with their pros and cons to help the reader understand different image crypto-stego techniques. This paper also gives insight into the evaluation parameters being used for steganography and cryptography analysis. This review is significantly highlights the grey areas in the area of the image steganography. Most of the existing image steganography approaches still suffer in terms of less security, low payload, poor image quality, and complexity. Therefore, it is a necessity to develop efficient image steganographic schemes along with data encryption to improve image embedding capacity, image imperceptibility, security with less complexity and should be resistant to different attacks.

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Jan, A., Parah, S.A., Hussan, M. et al. Double layer security using crypto-stego techniques: a comprehensive review. Health Technol. 12 , 9–31 (2022). https://doi.org/10.1007/s12553-021-00602-1

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Received : 03 August 2021

Accepted : 20 September 2021

Published : 13 October 2021

Issue Date : January 2022

DOI : https://doi.org/10.1007/s12553-021-00602-1

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