DCT Based Texture Synthesis Oriented Steganography

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1 DCT Based Texture Synthesis Oriented Steganography SK. Karthika Assistant Professor, CSE TRP Engineering College G.Subashini Department of CSE TRP Engineering College P.B.Shanmathi Department of CSE TRP Engineering College ABSTRACT: Steganography is an art of conveying messages in a secret way such that only the receiver knows the existence of the message and synthesis is the process of re sampling a smaller source into a of bigger size that has property same as that of the source. In the proposed work, taking advantage of synthesis, it is employed in Steganography to generate s carrying the secret message. In this work, is synthesized in transformed domain, Discrete Cosine Transform (DCT). The s are of arbitrary size which adds flexibility to the method. This proposed method also offers a good embedding capability and security. Unlike other existing methods which make use of input to hide the secret message, this method generates the. The experimental results prove that the proposed method is produces s of good visual imapct and consumes less computation time. Keywords: Steganography, synthesis, DCT, embedding, stego. I. INTRODUCTION Steganography is defined as the technique of concealing information within carriers. It is the art and science of writing secret messages in a way that no one, apart from the sender and intended recipient can suspect the existence of the message. The cover medium can be an or an audio or a video or a text. The ultimate aim of Steganography is imperceptibility, that is, the embedded message should not be discernible to the human eye. There are two other goals: Embedding capacity and security. Image Steganography can be implemented either in spatial domain or in frequency domain. Spatial domain based Steganography techniques are vulnerable to attacks and are thus less secure. Frequency domain based Steganography techniques add an additional layer of security. Transformations like Discrete Cosine Transform (DCT), Discrete Fourier Transform (DFT) and Discrete Wavelet Transform (DWT) can be utilized for Steganography. Texture is a ubiquitous visual occurrence. It can describe a wide assortment of surface characteristics such as plants, terrain, minerals, skin and fur. Textures are commonly engaged when rendering synthetic im Texture synthesis is an alternative way to create s. Synthetic s can be made of any size, without visual repetition. There are good methods available for synthesizing s and some of the important methods include tiling, stochastic synthesis, single purpose structured synthesis, patch based synthesis and pixel based synthesis. In this paper, a patch based synthesis based Steganography is proposed. The is synthesized in frequency domain. The synthesis process weaved into Steganography to conceal secret messages as well as the source. This process also allows us to extract the secret messages and the source from a stego synthetic. The proposed approach offers four advantages. First, payload which our scheme offers is proportional to the size of the stego. Secondly, a steganalytic algorithm is not likely to defeat this proposed method. Third, the reversible capability allows easy recovery of secret message and source. Finally, the computation time consumed by our proposed Steganography system is comparatively less since less number of transformed coefficients are considered for synthesizing. The rest of this paper is organized as follows: In Section II, literature survey is presented. In Section III, we have discussed the proposed embedding and extracting architectures in detail. The performance ISSN: Page 40

2 measures are described in section IV. We have summarized the experimental results Section V, followed by conclusions in the final section. II. LITERATURE SURVEY Numerous Steganographic methods have been developed in frequency domain. In frequency domain, the pixels of the are represented as transformed coefficients such that there is no correlation between them. The transformed coefficients of the frequency domain can be obtained using transformations like Discrete Cosine Transformation (DCT), Discrete Fourier Transformation (DFT) and Discrete Wavelet Transformation (DWT). Chin-Chen Chang et al., [1], suggested a reversible Steganography method using side match vector quantization focusing on high capacity of embedding. In [2], the authors have employed Modified Matrix Encoding (MME) for Steganography with minimal distortion. J. Fridrich et al., in [3], have discussed the dead ends, challenges and opportunities of Steganography with JPEG s. In [4], a data hiding method combining spread spectrum technique and frequency domain technique is reported. This method is computationally complex. A fine coded and coarsely coded data hiding scheme is presented in [5] and it provides good security when compared to the previously discussed techniques. In [6], the authors have outlined a Steganography system for JPEG2000 compressed s. This system addresses capacity issues. C.C. Lee et al., in [7], have reported a Steganographic scheme based on Search Order Coding (SOC) and Vector Quantization (VQ). In this method the capacity was increased to 6-bit per pair of blocks with a good quality. To solve the issues in large embedding rate with minimal distortion, Weiming Zhang et al., in [8], have suggested a technique that generates binary stego codes for embedding. Wei-Jen Wang et al., in [9], have made a detailed study on various data hiding methods for VQ based s. T. Filler et al., [10], have used Syndrome Trellis Codes (STC) for minimizing the additive distortion. In [11], uniform embedding in all coefficients of transformed domain is adopted to increase embedding rate. On combining the concepts of STC and uniform embedding, Linjie Guo et al., in [12], have suggested a high capacity, high imperceptibility Steganography system. Texture synthesis has received a lot of concentration recently in computer vision and graphics [13]. The most recent work has focused on synthesis by example, wherein a source is re sampled using either pixel-based or patch-based algorithm to produce a new synthesized. Pixel-based algorithms [14] [16] generate the synthesized, pixel by pixel and makes use of spatial neighborhood comparisons to choose the most similar pixel in a sample as the output pixel. Patch-based algorithms [17], [18] paste patches from a source instead of a pixel to synthesize s. This approach of Cohen et al. and Xu et al. improves the quality as structures inside the patches are maintained. However, as patches are pasted with a small overlapped region during the synthetic process, an effort must be took to ensure that the patches agree with their corresponding neighbors. Liang et al. [19] have reported a patch-based sampling strategy and have used the feathering approach for the overlapped areas of adjacent patches. Ni et al. [20] reported an reversible data hiding algorithm that has a capability to recover the cover without any distortion from the stego after extracting the hidden data. Histogram shifting is a preferred technique among existing approaches of reversible data hiding since it controls the modification on pixels, therefore reducing the embedding distortion. Recently Kuo Chen Wu et al., [21] have reported a synthesis based Steganography, where message oriented synthesis is performed. The authors have used the original pixels to perform patch based message oriented synthesis. This method is reversible and is secure against certain geometric attacks. It also produce varying sized stego synthetic s, but this method is computationally complex as it involves computation of each pixel to obtain the stego synthetic. Motivated by the fact that the existing methodologies do not focus on all steganographic issues and the fact that they are vulnerable to steganalytic algorithms, in this paper we propose a DCT based synthesis oriented Steganography that focuses on all Steganography issues and resistant to steganalytic algorithms. Here, we present our work ISSN: Page 41

3 that takes advantage of the patch-based methods to embed a secret message during the synthesizing procedure thus allowing the source to be recovered in the message extraction procedure, providing reversibility. The detailed description of the proposed method is given in the next section. III. PROPOSED STEGANOGRAPHY METHOD 3.1 Architecture Source Non overlappi ng source patches Pre stego Index table The source serves as the input to our Steganography method. The basic unit of the proposed steganographic synthesis is referred to as a patch. A patch is an block of a source whose size is user-specified. Once the source is split into non overlapping blocks called patches, they are weaved into an empty called as pre stego. The size of the pre stego is chosen based on the size of the secret message to be embedded. The location of each patch in the pre stego is recorded in an index table which will serve as the key for the receiver to extract the secret message in proper order. Next, candidate patches are generated from the source. Candidate patches are those which are generated by scanning the source with a fixed window size along the scan line by moving through each pixel after which DCT is applied on the source patches and candidate patches. Euclidean distance is computed between the diagonal coefficient values of each patch and diagonal coefficient values of all the candidate patches. Followed by calculating the Euclidean distances, we rank them according to their distance such that the candidate patch with minimum distance will be ranked first. The final task is to synthesize the empty area in the pre stego around the source patches that are already weaved. Message oriented synthesis is employed to fill the empty areas around the source patches. The candidate patch having a rank that is equivalent to the decimal value of the characters in the secret message is embedded in the area that is to be synthesized. The steps are repeated until all the empty blocks in pre stego are filled. The final output will be a stego synthetic carrying the secret message. The architecture of the proposed DCT based synthesis oriented Steganography is presented in Fig 3.1. Candida te patches D C T Secret message Euclidean distance of diagonal values Rank the distances Texture synthesis Figure 3.1 Architecture for embedding in the proposed Steganography system For extracting the secret message, first the source patches are retrieved from the stego synthetic with the help of the index table that is shared between the sender and receiver. Then, the source patches are grouped to form the source. Once the source patches are removed from the stego, the resultant will be the pre stego. Next candidate patches are generated from the source after which DCT is applied on the source patches, candidate patches and the patches on the pre stego. Following this, Euclidean distance is computed between diagonal ISSN: Page 42

4 coefficient values of source patches and diagonal coefficient values of all the candidate patches. Now the distances are ranked. The next step is to compare diagonal values of the patches in the stego with the diagonal values of candidate patches. The rank of the matching candidate patch is the decimal equivalent of the secret message. This process is repeated for all the remaining patches in the pre stego. Fig 3.2 shows the architecture for extraction in the proposed method. Pre stego Inde x table Source patche s Source Figure 3.2 Architecture for extraction in the proposed Steganography system 3.2 Message Embedding Euclide an distance The detailed description of message embedding process of the proposed method is given in this section. The secret message and the source are the input to the embedding process. The first step is to split the source into non overlapping blocks of fixed size and the blocks are the source patches. Let ST w, ST h denote the width and height of source and SP w, SP h denote the width and height of the source patches respectively. The number of source patches obtained from the source is computed from equation (1). D C T Candida te patch Ra nk in g Comp arison and matchi ng Secret messa ge (1) Third an empty called as pre stego is generated. The size of the pre stego is chosen depending on the size of the secret message. The number of embeddable blocks in the pre stego is determined from equation (2). Number of embeddable blocks = Number of blocks in pre stego Number of source patches. (2) where number of blocks in pre stego is, Number of blocks in pre stego = Number of source patches + Size of the secret message..(3) The index table generation is the third step in the embedding process. Index table contains details of locations where the source patches are to be weaved. The source patches are randomly seeded in sparse arrangement so as to increase the security. The fourth step is generation of candidate patches. An arbitrary window size is taken to scan the source in scan line order and the resulting patches through scanning are called as candidate patches. The number of candidate patches is computed as,..(4) where WI w and WI h are the width and height of the window taken to scan the source. After generating the candidate patches and source patches, apply DCT on them. The resultant coefficients are highly uncorrelated. Next, the Euclidean distance between the diagonal values of source patches and all the candidate patches are computed and ranked. Let P 1, P 2,..,P n be the diagonal values of source patch and Q 1, Q 2,..., Q n be the diagonal values of candidate patch. The formula to calculate Euclidean distance between a point P n in source patch and a point Q n in candidate patch is given as, D(P, Q) =( ) 1/2. (5) The candidate patch with minimum distance from the source patch will be ranked first. Followed by this, the candidate patch having a rank equivalent to decimal value of characters in secret message is taken and weaved into the pre stego. The steps are repeated until all empty areas in pre stego are synthesized. The final is the that is ready to be sent to the receiver. ISSN: Page 43

5 3.3 Message Extraction To extract secret message from the stego, the first step is to extract the source. The source patches are retrieved from the stego with the help of index table that is shared between the sender and receiver. Since the index table contains information about details of source patches in stego, it helps in retrieval of source. Once the source patches are obtained from the stego, they are grouped in order to generate the source. The that remains after removing the source patches is the pre stego that contains the secret message. Next candidate patches are generated from the source as described in the previous section. After generating the candidate patches, DCT is applied on the source patches, candidate patches and the patches in pre stego. As specified in section 3.2, Euclidean distance between the diagonal values of source patches and candidate patches are computed and then ranked. Now, the patches in the pre stego are taken and their diagonal values are compared with the candidate patches. The rank of the matching candidate patch is the decimal equivalent of the characters in the secret message. These steps are repeated till retrieving the entire message. IV. PERFORMANCE MEASURE 4.1 Payload Determination Payload is the amount of bits that can be embedded in the. The payload capacity of the proposed method can be determined from the formula, Payload capacity = Bits embedded per patch x Number of embeddable patches in the stego..(6) 4.2 Quality of the As the stego is generated by the user, its quality can be determined from a measure called as structural similarity (SSIM) index. This measure quantifies the similarity between the pure and s. The measure between two windows x and y of common size N N is: (7) where, is the average of, is the average of, is the variance of, is the variance of, is the covariance of and,, are the two variables to stabilize the division with weak denominator, is the dynamic range of the pixel-values, k 1 = 0.01 and K 2 = 0.03 by default. V. EXPERIMENTS AND RESULTS The proposed DCT based synthesis oriented Steganography system has been texted with more than 200 s. Three such s viz. Grass, rope net and metal of size (64x64) with pixel values in the range are presented in Fig (a) (b) (c) Figure 5.1 Source s (a) Grass (b) Rope net (c) Metal The source is partitioned into non overlapping blocks called source patches of size (8x8). Next, an empty called pre stego is generated with number of embeddable patches equal to the size of the secret message. The number of embeddable patches can be calculated from equation (2), given in section 3.2. Now, the source patches are weaved into the pre stego at sparse locations and it is made a note in the index table which serves as a key to the receiver. The index table holds the details of locations of each source patch in the pre stego. Next candidate patches are generated from the source as described in section 3.1. The number of candidate patches generated from a source of specific size can be computed from equation (4) as mentioned in section 3.2. Followed by this, DCT is applied on the source patches and candidate patches after which the Euclidean distance between the diagonal coefficients of source patch and diagonal coefficients of candidate patch is computed using the formula (5) as mentioned in section 3.2. Then, the distances are ranked such that the candidate patch with minimum distance from the source patch is ranked first. These ranks are now used for message oriented synthesis. The candidate patch with a rank equal to the decimal ISSN: Page 44

6 value of the characters of secret message is weaved into the pre stego. The steps are repeated till all characters of the secret message are embedded. The output is the stego that has been synthesized. The stego s of size (192x192) for the s given in Fig 5.1 are given in Fig 5.2. (a) (b) Input size=(64x64), Source patches =64, Candidate patches = 3249 Size of Count of patches in syntheti c, SSTn Embeddab le Patch Count, EPC 5 BPP Total capacity 8 BPP 120x x x Figure 5.2 s generated by the proposed method for the s presented in Fig. 5.1 (a) Grass (b) Rope net (c) Metal s of various sizes viz. (120x120), (192x192), (240x240), (256x256), (512x512), (1024x1024) are generated by the proposed DCT based synthesis oriented Steganography with input source of size (64x64). The payload capacity is same for all stego s of same size and is computed as described in section 4.1. These various sizes are tested with payload capacities, 5 bits per patch (BPP) and 8 bits per patch. Payload capacities of 805 BPP, 2560 BPP, 4180 BPP, 4800 BPP, BPP, BPP with 5 bits being embedded per patch are obtained for stego s of size (120x120), (192x192), (240x240), (256x256), (512x512) and (1024x1024). For 8 bits embedded per patch, the payload capacities observed are 1288 BPP, 4096 BPP, 6688 BPP, 7680 BPP, BPP, BPP. These results are tabulated in Table 5.1 (c) 256x x x Table 5.1 Total Payload Capacity with the proposed Steganography method The performance of the proposed Steganography method is evaluated by computing quality of the stego that is synthesized. The quality of the stego is computed as mentioned in section 3.2. The similarity measure will be in the range [-1, 1]. A result of 1 indicates excellent quality, a result in the range 0.5 to 1 indicates a good quality and -1 refers to poor quality. Structural similarity factor of 0.814, and are obtained for Grass, Rope net and Metal s respectively with 5 bits per patch. For 8 bits per patch, structural similarity factor of 0.945, and are observed for Grass, Rope net and Metal s respectively. Table 5.2 shows the observed results. ISSN: Page 45

7 Texture Source Vs. with 5 BPP Table 5.2 Structural similarity obtained with the proposed Steganography technique It is evident from the results that high payload and good quality stego is produced by the proposed Steganography system. A maximum payload of bits is obtained for stego of size (1024x1024) with 8 BPP. Also on an average the quality of the stego is in the range 0.8 to 0.9 which is a good quality. VI. CONCLUSION In this paper, a DCT based synthesis oriented Steganography is proposed. This method employs DCT and patch based synthesis to generate the stego. Taking a small source as input, the proposed Steganography system produces stego s of bigger size carrying the secret message. This Steganography system addresses important Steganography issues like imperceptibility, payload, security and to some extent robustness. The quality of the constructed stego is also good with the proposed technique. REFERENCES Source Vs. with 8 BPP Grass Rope net Metal [1] Chin-Chen Chang, Wei-Liang Tai and Chia-Chen Lin, A reversible data hiding scheme based on side match vector quantization, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 14, No. 10, pp , [2] Y. Kim, Z. Duric and D. Richards, Modified matrix encoding technique for minimal distortion Steganography, Proceedings 8th Information Hiding Conference, Vol. 4437, pp , [3] J. Fridrich, T. Penvy and J. Kodovsky, Statistically undetectable JPEG Steganography: Dead ends challenges and oppurtunities, Proceedings of 9th ACM Workshop Multimedia Security, pp.3-14, [4] Chun-Hsiang Huang, Shang-Chih Chuang and Ja-Ling Wu, Digital invisible ink data hiding based on Spread spectrum and Quantization techniques, IEEE Transactions on Multimedia, Vol. 10, No. 4, pp , [5] Zhiyuan Zhang, Ce Zhu and Yao Zhao, Two description coding with Steganography, IEEE Signal Processing Letters, Vol. 15, pp , [6] Liang Zhang, Haili Wang and Renbiao Wu, A high capacity Steganography scheme for JPEG2000 Baseline system, IEEE Transactions on Processing, Vol. 18, No. 8, pp , [7] C.C. Lee, W.H. Ku and S.Y. Huang, A new Steganographic scheme based on vector quantisation and search order coding, IET Image Processing, Vol. 3, Iss. 4, pp , [8] Weiming Zhang, Xinpeng Zhang and Shuozhong Wang, Near optimal codes for information embedding in gray scale signals, IEEE Transactions on Information Theory, Vol. 56, No. 3, pp , [9] Wei-Jen Wang, Cheng-Ta Huang and Shiuh-Jeng wang, VQ applications in Steganographic data hiding upon multimedia s, IEEE Systems Journal, Vol. 5, No. 4, pp , [10] Thomas Filler, Jan Judas and Jessica Fridrich, Minimizing additive distortion in Steganography using syndrome trellis codes, IEEE Transactions on Information Forensics and Security, Vol. 6, No. 3, pp , [11] L. Guo, J. Ni and Y.Q. Shi, An efficient JPEG Steganographic scheme using Uniform embedding, Proceedings of 4th IEEE International Workshop on Information Forensics Security, pp , [12] Linjie Guo, Jiangqun Ni and Yun Qing Shi, Uniform embedding for efficient JPEG Steganography, IEEE Transactions on Information Forensics and Security, Vol. 9, No. 5, pp , [13] Regunathan Radhakrishnan, Mehdi Khauazi and Nazir Memon, Data masking: A new approach for steganography, Journal of VLSI Signal Processing, Springer, Vol. 41, pp , [14] Yang Jie, Algorithm of Image Information Hiding Based on New Anti-Arnold transform and Blending in DCT Domain, 12 IEEE conference on communication technology, pp , [15] Jessica Fridrich, Minimizing the embedding impact in steganography, Proceedings of ACM multimedia security 06, pp , [16] T. Pevny and J. Fridrich, Determining the stego algorithm for JPEG s, IEE Proc.-Inf. Secur., Vol. 153, No. 3, pp , [17] Zhiyuan Zhang, Ce zhu and Yao zhao, Two description coding with steganography, IEEE Signal Processing Letters, Vol. 15, No. 5, pp , [18] Liang Zhang, Haili Wang, and Renbiao Wu, A High- Capacity Steganography Scheme for JPEG2000 Baseline System, IEEE Transactions on Image Processing, Vol. 18, No. 8, pp , [19] A.Nag, S. Biswas, D. Sarkar and P.P. Sarkar, A novel technique for steganography based on block DCT and Huffman Encoding, International Journal Computer Science and Information Technology, Vol. 2, No. 3, pp , [20] Fangjun Huang, Jiwu Huang and Yun-Qing Shi, New Channel Selection Rule for JPEG Steganography, IEEE Transactions on Information Forensics and Security, Vol. 7, No. 4, pp , [21] Kuo Chen Wu and Chung Ming Wang, Steganography using reversible synthesis, IEEE Transactions on Image Processing, Vol. 24, no. 1, pp , ISSN: Page 46