Abstract
The author analyzes the problem of detecting the adaptive steganography by the J-UNIWARD method by steganoanalytical systems based on machine learning. As determined by the comparative analysis of the accuracy, the statistical models of constructing characteristic vectors that are calculated in the spatial domain, such as GFR, PHARM, and DCTR, are most sensitive to J-UNIWARD. Here, two following ways to improve the accuracy of steganoanalysis based on these models are proposed: via the analysis of the most probable embedding locations and via the balanced vote on the three models. Significant degradation of the accuracy of steganoanalysis without preliminary classification of images according to their parameters is demonstrated. The obtained results can be used to generate efficient steganoanalysis systems for JPEG images.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
V. Holub, J. Fridrich, and T. Denemark, “Universal distortion function for steganography in an arbitrary domain,” EURASIP J. Inform. Security, Article number 1 (2014). https://doi.org/10.1186/1687-417X-2014-1.
T. Filler, J. Judas, and J. Fridrich, “Minimizing embedding impact in steganography using trellis-coded quantization,” in: Proc. SPIE, Electronic Imaging, Media Forensics and Security II (San Jose, USA, Jan 27, 2010), Vol. 7541, 754105 (2010). https://doi.org/10.1117/12.838002.
V. Sidorenko and V. Zyablov, “Decoding of convolutional codes using a syndrome trellis,” IEEE Transactions on Information Theory, Vol. 40, No. 5, 1663–1666 (1994). https://doi.org/10.1109/18.333887.
C. Chen and Y. Q. Shi, “JPEG image steganalysis utilizing both intrablock and interblock correlations,” in: IEEE Intern. Symp. on Circuits and Systems, ISCAS 2008 (Seattle, USA, May 18–21, 2008), IEEE (2008), pp. 3029–3032. https://doi.org/10.1109/ISCAS.2008.4542096.
J. Kodovský and J. Fridrich, “Calibration revisited,” in: Proc. 11th ACM Workshop on Multimedia and Security (Princeton, USA, Sept 2009), ACM, New York (2009), pp. 63–74. https://doi.org/10.1145/1597817.1597830.
Q. Liu, “Steganalysis of DCT-embedding based adaptive steganography and YASS,” in: Proc. of the 13th ACM Multimedia Workshop on Multimedia and Security (Buffalo, USA, Sept 2011), ACM, New York (2011), pp. 77–86. https://doi.org/10.1145/2037252.2037267.
T. Pevny and J. Fridrich, “Merging Markov and DCT features for multiclass JPEG steganalysis,” in: Proc. SPIE, Electronic Imaging, Security, Steganography, and Watermarking of Multimedia Contents IX (San Jose, USA, March 2, 2007), Vol. 6505, 650503 (2007). https://doi.org/10.1117/12.696774.
J. Kodovský and J. Fridrich, “Steganalysis in high dimensions: fusing classifiers built on random subspaces,” in: Proc. SPIE, Media Watermarking, Security, and Forensics III (San Francisco, USA, Jan 23–27, 2011), Vol. 7880, 78800L (2011). https://doi.org/10.1117/12.872279.
X. Song, F. Liu, C. Yang, X. Luo, and Y. Zhang, “Steganalysis of adaptive JPEG steganography using 2D Gabor filters,” in: Proc. 3rd ACM Workshop on Information Hiding and Multimedia Security (Portland, USA, June 2015), ACM, New York (2015), pp. 15–23. https://doi.org/10.1145/2756601.2756608.
V. Holub and J. Fridrich, “Low complexity features for JPEG steganalysis using undecimated DCT,” IEEE Transactions on Inform. Forensics and Security, Vol. 10, No. 2, 219–228 (2015). https://doi.org/10.1109/TIFS.2014.2364918.
V. Holub and J. Fridrich, “Phase-aware projection model for steganalysis of JPEG images,” in: Proc. SPIE, Media Watermarking, Security, and Forensics (San Francisco, USA, Feb 8–10, 2015), Vol. 9409, 94090T (2015). https://doi.org/10.1117/12.2075239.
J. Kodovský, J. Fridrich, and V. Holub, “Ensemble classifiers for steganalysis of digital media,” IEEE Trans. on Inform. Forensics and Security, Vol. 7, No. 2, 432–444 (2012). https://doi.org/10.1109/TIFS.2011.2175919.
N. V. Koshkina, “Comparison of efficiency of statistical models used for formation of feature vectors by JPEG images steganalysis,” Theoretical and Applied Cybersecurity, Vol. 2, No. 1, 22–28 (2020). https://doi.org/10.20535/tacs.2664-29132020.1.
N. V. Koshkina, “Research of main components of machine learning based JPEG-stegananalysis systems,” Ukrainian Information Security Research J., Vol. 22, No. 2, 97–108 (2020). https://doi.org/10.18372/2410-7840.22.14801.
X. Song, F. Liu, L. Chen, C. Yang, and X. Luo, “Optimal Gabor filters for steganalysis of content-adaptive JPEG steganography,” KSII Trans. on Internet and Information Systems, Vol. 11, No. 1, 552–569 (2017). https://doi.org/10.3837/tiis.2017.01.029.
Author information
Authors and Affiliations
Corresponding author
Additional information
Translated from Kibernetyka ta Systemnyi Analiz, No. 3, May–June, 2021, pp. 184–192.
Rights and permissions
About this article
Cite this article
Koshkina, N.V. J-UNIWARD Steganoanalysis. Cybern Syst Anal 57, 501–508 (2021). https://doi.org/10.1007/s10559-021-00374-6
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10559-021-00374-6