AN ANALYSIS OF VARIOUS STEGANOGRAPHIC ALGORITHMS Chinchu Elza Andrews 1, Iwin Thanakumar Joseph 2 1. Post Graduate Student, Department of Computer Science and Engineering, Karunya University, India 2. Assistant Professor, Department of Computer Science and Engineering, Karunya University, India Abstract- Now a days there is a rapid growth in the field of digital communication. Steganography is a covert communication that in which the existance of the message is not known other than the sender and the receiver. The secret communication is happenning through the multimedia carrier like audio, video, digital images etc. Here in this paper it provides an analysis of various steganographic algorithms for digital images, mainly covering the fundamental concepts of steganography,and the survey of various steganographic methods for images. Keywords: Stegaography,Digital image, information hiding, steganalysis I.INTRODUCTION Steganography is a secret communication approach that can transmit information without arousing suspicion of the existence of the secret communication. The carrier of steganography can be various kinds of digital media such as image, audio, and video, etc. Steganography means art of covered writing. For a long, steganography was rudimentary and its use was exclusively reserved to the military and secret services. This huge amount of information allows to hide easily some messages and to communicate in a discreet way. The main aim in steganography is to hide the very existence of the message in the cover medium. Steganography includes a vast array of methods of secret communication that conceal the very existence of hidden information. Fig 2 Types of Steganography Steganalysis is the art and science of detecting messages hidden using steganography. The main aim of steganalysis is to identify suspected packages, determine whether or not they have a payload encoded into them, and, if possible, recover that payload. Hence, the major challenges of effective steganography are:- 1. Security of Hidden Communication: In order to avoid raising the suspicions of eavesdroppers, while evading the meticulous screening of algorithmic detection, the hidden contents must be invisible both perceptually and statistically. 2. Size of Payload: As steganography is for secret communication and therefore usually requires sufficient embedding capacity. 116
Requirements for higher payload and secure communication are often contradictory. Due to the potential abuse by criminals and terrorists, much research has also gone into the field of steganalysis the art of detecting and deciphering a hidden message. As many novel steganographic hiding algorithms become publicly known, researchers exploit these methods by finding statistical irregularities between clean digital images and images containing hidden data. An ideal steganographic system is undetectable in a sense that images containing hidden data are detected with accuracy no better than random guessing. Further, to prevent future steganalytic attacks, an ideal steganographic system produces stego-images which are statistically identical to clean images. All of these requirements for a secure steganographic system must be accomplished despite the fact the hiding algorithm is publicly known. While pure steganography does not involve the use or exchange of any secret information such as stego-keys, both secret key steganography and public key steganography rely on the sharing of such keys. In a secret key steganographic system, a sender hides a secret message into a cover object using a secret key. The key used in the embedding process can also be used to reverse the process in order to extract the hidden data. In this steganographic system, it is assumed that the communicating parties are able to transmit secret keys over a secure channel. Finally, public key steganography models after public key cryptography. 1.2 IMAGE STEGANOGRAPHY Images are considered as the most popular file formats used in steganography. They are known for constituting a noncausal medium, due to the possibility to access any pixel of the image at random. 1.1 FUNDAMENTALS OF STEGANOGRAPHY Three fundamental steganographic systems are defined: a pure steganographic system, a secret key steganographic system, and a publickey steganographic system. In pure steganography, no keys is needed for the communicating parties other than the embedding and extracting algorithms. The security of such a system relies on the secrecy of the embedding and extracting algorithms. Fig:.1 A generalized steganographic framework Fig 3 Image Steganography Data hiding have general requirements: Imperceptibility: marked and original data source should be perceptually identical Robustness : The embedded data should survive any attacks. Capacity : Maximum data embedding payload Security : Security is in the key. Image steganography schemes are classified into two categories: 117
Spatial-domain based. Transform-domain based. 1.3 JPEG COMPRESSION The compression of JPEG images contains several processes: 1. Converting pixel values to YCbCr. 2. Downsampling the chrominance values. 3. Transforming values to frequencies. 4. Quantisation. 5. Zig-Zag ordering. 6. Lossless Compression. a. Converting pixel values to YCbCr The first phase is to convert the RGB colour of the image into three different components (Y, Cb, and Cr). The Y component relates to the luminance (brightness), and the U and V elements relate to chrominance (colour). The chrominance coefficients of an image (Cb and Cr) are determined by a 2D grid that has blue to yellow on one axis, and red to green on another. b. Down sampling the chrominance values The second phase of JPEG compression is to downsample the image.down sampling is the process of reducing the quality of a signal with the goal of making the file size smaller. It is a common belief that the human eye is more sensitive to changes in brightness than to changes in colour.jpeg compression often downsamples by taking 4 adjacent pixels and averaging them to one value. All of this can be done without any noticeable change in the quality of the image. c. Transforming values to frequencies The Discrete Cosine Transform (DCT) is used for JPEG images to transform them into frequencies. By modifying a single DCT coefficient, the entire 64 pixels in that block will be affected.the largest value is located in the top-left corner of the block, this is the lowest frequency. The value is so high because the data is encoded with the highest importance and the lowest frequency. This, in simpler terms means that this value is the average value of all the pixel values in this block. The values are typically always high around the top-left corner of a DCT block but note that the numbers closest to zero seem to populate around thelower-right corner of the block. These are the high frequencies and it is these values that are removed during the next step. d. Quantisation Quantisation is the process of taking the remaining coefficients and dividing them individually against a predetermined set of values and then rounding the results to the nearest real number value.the higher these pre-determined values are determines how much detail will be removed from the image. The higher the numbers, the more detail is eliminated. The goal is to eliminate the high frequency (lower-right) values by eliminating the values that would not make a difference to the image should they not exist, a great deal of data can be removed with minimum effect on the quality. e. Zig-Zag ordering There are only a few values that hold numbers other than zero - the majority will always be zeros. Fig 4. Zig-Zag Ordering 118
It is also typical to see the non-zero numbers in the upper left, and zeros as you get towards the lower-right corner. As this is the case, this stage of JPEG compression reorders the values using a zig-zag type motion so that similar frequencies are grouped together. f. Lossless Compression The last process involves the use of two different algorithms. Run-Length Encoding (RLE) compresses the high frequency coefficients and a Differential Pulse Code Modulation (DPCM) compresses the first low frequency coefficient. A Huffman algorithm is then used to compress everything. Finally, the Huffman trees are stored in the JPEG header. 2 STEGANOGRAPHY ALGORITHMS 2.1 Hide & Seek: The Sequential Approach The simplest form of image steganography is the method known as Hide & Seek which replaces the LSBs of pixel values (also referred to as the spatial domain) with the bits from the message bit stream.the algorithm is so straightforward that it does not require a key to be implemented.whilst this makes things a lot simpler to program and exchange the secret, it does mean that the security lies solely in the algorithm. If a key were used, then it might still be impossible for the adversary to decode the hidden message, as the key would usually index the manipulated regions of the image. In the case of the Hide & Seek algorithm however, the adversary simply needs to understand how the algorithm works, and they will be able to decrypt the message. Advantages Does not require a key to be implemented. Too much simple to program and exchange the secret. Disadvantages Hide & Seek method was not considered very secure. 2.2 Hide & Seek: The Randomised Approach The randomised approach to the Hide & Seek algorithm makes it possible to scatter the locations of the pixels that are to be replaced with the message data. The core of the encoding process is identical to that of the sequential method. In fact, the two methods only differ in terms of how the image data ci is presented before the embedding process starts. For the randomised approach the image data c is usually shuffled using a Pseudo Random Number Generator (PRNG). This generator will take the image data c and produce a shuffled version C according to a seed k that is specified by the encoder. There will also be an inverse shuffle which takes C and returns the original order c when the same k is used. The values are then shuffled back to their original positions after embedding such that the image can be displayed properly for sending it across some communications channel to the recipient. 2.3 JSteg/JPHide : JSteg and JPHide are two classic JPEG steganographic tools that employ the LSB embedding technique. JSteg functions to hide the secret data in a cover image by simply exchanging the LSBs of non-zero quantized DCT coefficients with secret message bits. The quantized DCT coefficients, already used to conceal secret message bits in JPHide, are selected randomly by a pseudo-random number generator. JPHide, on the other hand, tends not only to modify the LSBs of the selected 119
coefficients, but it can also switch to a process where bits of the second leastsignificant bit-plane are likely to be worked out.the JSteg algorithm was developed by Derek Upham and is essentially a carbon copy of the Hide & Seek algorithm, because it employs sequential least significant bit embedding. In fact, the JSteg algorithm only differs from the Hide & Seek algorithm because it embeds the message data within the LSBs of the DCT coefficients of c, rather than its pixel values.before the embedding process begins, the image is converted to the DCT domain in 8x8 blocks such that the values of ci switch from pixel values to DCT coefficients. In order for the values to be presented as whole numbers, each 8x8 block is quantised according to a Quantisation Table Q. The result is where the embedding algorithm operates. There are two types of coefficient in every 8x8 block: DC and AC. The value at the top left of each 8x8 block is known as the DC coefficient. It contains the mean value of all the other coefficients in the block, referred to as the AC coefficients. The DC coefficients are highly important to each block as they give a good estimate as to the level of detail in the block. Changing the value of the DC coefficient will also change many of the values of the AC coefficients, and this will create a visual discrepancy when the image is converted back to the spatial domain and viewed normally. For this reason, the JSteg algorithm does not embed message data over any of the DC coefficients for every block. In addition to this, the algorithm also does not permit embedding on any AC coefficient equal to 0 or 1. Employ the LSB embedding technique. To hide the secret data in a cover image by simply exchanging the LSBs of non-zero quantized DCT coefficients with secret message bits. JPHide can switch to a mode where the bits of the second least significant bitplane are modified. Create a visual discrepancy when the image is converted back to the spatial domain and viewed normally. The JSteg algorithm does not embed message data over any of the DC coefficients. Does not permit embedding on any AC coefficient equal to 0 or 1. 2.4 F5 ALGORITHM: The F5 steganographic algorithm was introduced by (Westfeld.,1995). Rather than replacing the LSBs of quantized DCT coefficients with the message bits, the absolute value of the coefficient is reduced by the F5 algorithm by one if it needs modification. Due to the author's argument, the use the chi-square attack can never detect this type of embedding. In addition to embedding message bits into randomly chosen DCT coefficients, the F5 algorithm employs matrix embedding that reduces the number of changes necessary for hiding a message of a certain length. Both, the message length and the number of non-zero coefficients are required in the embedding process to determine the matrix embedding needed to decrease the number of modifications required in the cover image. Reduces the number of changes necessary for hiding a message of a certain length. Instead of LSBs of quantized DCT coefficients with the message bits, the absolute value of the coefficient is decreased by one for modification. Cannot be detected using the chisquare attack. Minimizes the number of modifications of the cover image. 120
more secure than the F3 and F4 algorithms Vulnerable to recompression. 2.5 OutGuess 0.1: OutGuess is provided by Provos as a UNIX source code for which there are two widely known released versions. Embedding the message data sequentially using the Hide & Seek method was not considered very secure, neither was the fact that the JSteg algorithm embedded in the same fashion.the first version of OutGuess, designed by Neils Provos [18], improved the JSteg algorithm by scattering the embedding locations over the entire image according to a PRNG on image c derived using seed k. This is very similar to the way that the randomised embedding approach improved the Hide & Seek algorithm. OutGuess-0.13b, which is exposed to statistical analysis OutGuess-0.2, which includes the ability to safeguard statistical properties.hereafter, OutGuess refers to OutGuess-0.2. There are two stages representing the embedding process of OutGuess. The first of which is that OutGuess embeds secret message bits along a random walk into the LSBs of the quantized DCT coefficients while skipping 0s and 1s.Soon after modifications are made to the coefficients already left during embedding to make the global DCT histogram of the stego image match that of the cover image. OutGuess cannot be subjected to a chi-square attack. OutGuess-0.2, which includes the ability to preserve statistical properties. OutGuess cannot be subjected to a chisquare attack. OutGuess-0.13b, which is vulnerable to statistical analysis. 2.6 F3 ALGORITHM: As an alternative to the OutGuess0.2 algorithm, (Andreas Westfeld,1999) designed an algorithm called F3 which was considered more secure. The reason is that it did not instantiate the same embedding process as the JSteg and OutGuess algorithms. Instead of avoiding embedding in DCT coefficients equal to 1, the F3 algorithm permitted embedding in these regions, whilst it would still avoid embedding in zeros and the DCcoefficients. The algorithm still embedded the message data sequentially within c. Another change with this algorithm was that it did not embed directly in the least significant bits of the DCT coefficients, but instead took the absolute value of the coefficients first, before comparing them to the message bits. If both the absolute value of the coefficient, and the message bit were the same, then no changes are made. If they are different, then the absolute value of the DCT coefficient is reduced by 1. An implication of this however, is that zero values are often created which the decoding algorithm will not be programmed to extract data from. The F5 algorithm worked around this by reembedding mi when the result is that a zero DCT coefficient is created. This reembedding was referred to as shrinkage. Effectively embedded more zeros than ones. Even more secure than OutGuess0.2. Occurance of re-embedding known as shrinkage. Model Based Steganography: Model-Based steganography (MB) can be defined as a general framework for conducting both steganography and steganalysis by simply using a statistical model of the cover media. The MB method for JPEG images is capable of having high 121
message capacity while remaining secure against many first-order statistical attacks. Achieves a high message capacity. Remains secure against several first order statistical attacks. 2.7 YASS. Yet Another Steganographic Scheme (YASS) belongs to JPEG steganography, but does not conceal data in JPEG DCT coefficients directly. Instead, an input image in the spatial domain is divided into blocks with a fixed large size, called Big blocks (Bblocks). A later stage is to randomly select within each B-block, an 8 8 sub-block known as embedding host block (or H- block). Then via using error correction codes, secret data is encoded and embedded in the DCT coefficients of the H-blocks. Finally, the entire image is compressed and distributed as a JPEG image after inversing DCT on the H-blocks. Yet Another Steganographic Scheme (YASS) belongs to JPEG steganography but it does not embed data in JPEG DCT coefficients directly.the self-calibration process, a powerful technique in JPEG steganalysis for estimating the cover image statistics, is disabled by YASS. Another advantage of YASS is that the embedded data may survive in the active warden scenario. Recently Yu et al proposed a YASS-like scheme to enhance the security performance of YASS via enhancing block randomization. using error correction codes, secret data is encoded and embedded in the DCT coefficients. The embedded data may survive in the active warden scenario. Does not conceal data in JPEG DCT coefficients directly. The self-calibration process, a powerful technique in JPEG steganalysis for estimating the cover image statistics, is disabled by YASS. 3. CONCLUSION In this paper, it explains about the fundamental concepts and notions as some typical techniques in steganography, Many different techniques exist and continue to be developed, the ways of detecting hidden messages also getting in advance. Steganography might also become limited underlaws, since governments already claimed that criminals use these techniques to communicate. So more and more researches are doing on this field of steganography. REFERENCES [1] Anderson. R. J. and Petitcolas.F.A.P,(1998), On the Limits of Steganography, IEEE Journal of Selected Areas in Communications, Special Issue on Copyright and Privacy Protection, vol.16(4), pp. 474 481 [2] Crandall. R, (1998), Some notes on steganography,available:http://os.inf.tudresden.de/westfeld/crandall.pdf,steganographymailing List [Online]. Available: [3] Fridrich. J., Goljan. M. and Soukal. D, (2004), Perturbed quantization steganography with wet paper codes, in Proc. ACM Workshop Multimedia and Security, Magdeburg, Germany, Sep. 20 21, pp.4 15 [4] Fridrich.J., Pevný. T. and Kodovský. J, (2007), Statistically undetectable JPEG steganography: Dead ends, challenges, and opportunities, in Proc. ACM Workshop on Multimedia and Security, Dallas, TX, Sep. 20 21, pp. 3 14. 122
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