Abstract
In recent time, most of the steganographic methods minimize the embedding cost while maximizing the embedding capacity by injecting message bits in the highly textured regions of the image. Recently, the Clustering Modification Direction (CMD) steganography has been proposed as a wrapper over the additive steganography algorithms, resulting in a substantial improvement in statistical imperceptibility against state-of-the-art steganalytic classifiers. In this paper, a steganalysis scheme, named Selective-Signal-Removal (SSR) is proposed to mount an attack on the CMD algorithm. It has been observed experimentally that the CMD scheme has a tendency to embed in a localized cluster having higher texture. The proposed scheme exploits this fact and tries to predict the embedding zones. It essentially discards the irrelevant region of the image (which may not be modified by the CMD algorithm while embedding) by using a heuristic function with an assignment algorithm to improve the steganalytic detection rate. The experimental results show that the proposed SSR scheme can detect CMD based steganography with improved accuracy.
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References
Ahmed N, Natarajan T, Rao KR (1974) Discrete cosine transform. IEEE Transactions on Computers C 23(1):90–93. https://doi.org/10.1109/T-C.1974.223784
Bae HJ, Jung SH (1997) Image retrieval using texture based on dct. In: Proceedings of ICICS, 1997 International conference on information, communications and signal processing. Theme: trends in information systems engineering and wireless multimedia communications (Cat., vol 2, pp 1065–1068). https://doi.org/10.1109/ICICS.1997.652144
Bas P, Filler T, Pevný T (2011) Break our steganographic system: the ins and outs of organizing boss. In: Proceedings of the 13th International conference on information hiding, IH’11. http://dl.acm.org/citation.cfm?id=?2042445.2042452. Springer, Berlin, pp 59–70
Boroumand M, Chen M, Fridrich J (2018) Deep residual network for steganalysis of digital images. IEEE Trans Inf Forensics Secur 14(5):1181–1193
Filler T, Judas J, Fridrich J (2011) Minimizing additive distortion in steganography using syndrome-trellis codes. IEEE Trans Inf Forensics Secur 6 (3):920–935. https://doi.org/10.1109/TIFS.2011.2134094
Fridrich J, Kodovsky J (2012) Rich models for steganalysis of digital images. IEEE Trans Inf Forensics Secur 7(3):868–882. https://doi.org/10.1109/TIFS.2012.2190402
Gujar S, Veni Madhavan C (2009) Measures for classification and detection in steganalysis
Holub V, Fridrich J (2012) Designing steganographic distortion using directional filters. In: 2012 IEEE International workshop on information forensics and security (WIFS). IEEE, pp 234–239
Holub V, Fridrich J, Denemark T (2014) Universal distortion function for steganography in an arbitrary domain. EURASIP Journal on Information Security 2014 (1):1. https://doi.org/10.1186/1687-417X-2014-1
Huang LY (2005) A fast method for textural analysis of dct-based image. J Inf Sci Eng, pp 181–194
Iatan IF (2010) The fisher’s linear discriminant. In: Borgelt C, González-Rodríguez G, Trutschnig W, Lubiano MA, Gil MÁ, Grzegorzewski P, Hryniewicz O (eds) Combining soft computing and statistical methods in data analysis. Springer, Berlin, pp 345–352
Kodovsky J, Fridrich J, Holub V (2012) Ensemble classifiers for steganalysis of digital media. IEEE Trans Inf Forensics Secur 7(2):432–444. https://doi.org/10.1109/TIFS.2011.2175919
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105
Li B, Wang M, Huang J, Li X (2014) A new cost function for spatial image steganography. In: 2014 IEEE International conference on image processing (ICIP). https://doi.org/10.1109/ICIP.2014.7025854, pp 4206–4210
Li B, Wang M, Li X, Tan S, Huang J (2015) A strategy of clustering modification directions in spatial image steganography. IEEE Trans Inf Forensics Secur 10(9):1905–1917
Pevny T, Bas P, Fridrich J (2010) Steganalysis by subtractive pixel adjacency matrix. IEEE Trans Inf Forensics Secur 5(2):215–224
Pevn? T, Filler T, Bas P (2010) Using high-dimensional image models to perform highly undetectable steganography. In: International workshop on information hiding. Springer, pp 161–177
Qian Y, Dong J, Wang W, Tan T (2015) Deep learning for steganalysis via convolutional neural networks. In: Media watermarking, security, and forensics 2015, vol 9409, pp 94090j. International society for optics and photonics
Qian Y, Dong J, Wang W, Tan T (2016) Learning and transferring representations for image steganalysis using convolutional neural network. In: 2016 IEEE International conference on image processing (ICIP), pp. 2752–2756. IEEE
Sedighi V, Cogranne R, Fridrich J (2015) Content-adaptive steganography by minimizing statistical detectability. IEEE Trans Inf Forensics Secur 11(2):221–234
Sedighi V, Cogranne R, Fridrich J (2016) Content-adaptive steganography by minimizing statistical detectability. IEEE Trans Inf Forensics Secur 11(2):221–234. https://doi.org/10.1109/TIFS.2015.2486744
Xu G, Wu HZ, Shi YQ (2016) Structural design of convolutional neural networks for steganalysis. IEEE Signal Process Lett 23(5):708–712
Ye J, Ni J, Yi Y (2017) Deep learning hierarchical representations for image steganalysis. IEEE Trans Inf Forensics Secur 12(11):2545–2557
Acknowledgements
Authors would like to thank the anonymous reviewers for their insightful comments and suggestions. Authors would also like to acknowledge the funding agency, Ministry of Human Resource Development, Government of India.
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Rawat, R., Singh, B., Sur, A. et al. Steganalysis for clustering modification directions steganography. Multimed Tools Appl 79, 1971–1986 (2020). https://doi.org/10.1007/s11042-019-08263-z
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DOI: https://doi.org/10.1007/s11042-019-08263-z