Steganalysis of content-adaptive JPEG steganography based on the weight allocation of filtered coefficients

Comparing with the steganalysis methods based on the feature sets assembled as histograms of filtered images, their improved versions incorporating the change probabilities of coefficients in the embedding domain provide more excellent performance for content-adaptive JPEG steganography, the weight allocation of feature statistical samples is the most important. In this paper, we propose a new weight allocation method in which the maximum change probability of corresponding correlation DCT coefficients is calculated as the weight of each filtered coefficient, and the final feature set is obtained by accumulating the weights in corresponding histogram statistical samples. Experimental results conducted on three modern content-adaptive JPEG steganographic schemes and the state-of-the-art steganalysis feature set indicate that the proposed method can improve the detection performance of original feature set markedly and is superior to the selection channel aware feature set.

[1]  Jessica J. Fridrich,et al.  Random Projections of Residuals for Digital Image Steganalysis , 2013, IEEE Transactions on Information Forensics and Security.

[2]  Jessica J. Fridrich,et al.  Minimizing Additive Distortion in Steganography Using Syndrome-Trellis Codes , 2011, IEEE Transactions on Information Forensics and Security.

[3]  Jessica J. Fridrich,et al.  Rich Models for Steganalysis of Digital Images , 2012, IEEE Transactions on Information Forensics and Security.

[4]  Jessica J. Fridrich,et al.  Further study on the security of S-UNIWARD , 2014, Electronic Imaging.

[5]  Yun Q. Shi,et al.  Uniform Embedding for Efficient JPEG Steganography , 2014, IEEE Transactions on Information Forensics and Security.

[6]  Tomás Pevný,et al.  "Break Our Steganographic System": The Ins and Outs of Organizing BOSS , 2011, Information Hiding.

[7]  Jessica J. Fridrich,et al.  Digital image steganography using universal distortion , 2013, IH&MMSec '13.

[8]  Jessica J. Fridrich,et al.  Steganalysis Features for Content-Adaptive JPEG Steganography , 2016, IEEE Transactions on Information Forensics and Security.

[9]  Jiwu Huang,et al.  New Channel Selection Rule for JPEG Steganography , 2012, IEEE Transactions on Information Forensics and Security.

[10]  Yi Zhang,et al.  Steganalysis of Adaptive JPEG Steganography Using 2D Gabor Filters , 2015, IH&MMSec.

[11]  Jessica J. Fridrich,et al.  Selection-channel-aware rich model for Steganalysis of digital images , 2014, 2014 IEEE International Workshop on Information Forensics and Security (WIFS).

[12]  Jessica J. Fridrich,et al.  Low-Complexity Features for JPEG Steganalysis Using Undecimated DCT , 2015, IEEE Transactions on Information Forensics and Security.

[13]  Jessica J. Fridrich,et al.  Phase-aware projection model for steganalysis of JPEG images , 2015, Electronic Imaging.

[14]  Bin Li,et al.  A new cost function for spatial image steganography , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[15]  Jessica J. Fridrich,et al.  Designing steganographic distortion using directional filters , 2012, 2012 IEEE International Workshop on Information Forensics and Security (WIFS).

[16]  Jiwu Huang,et al.  Adaptive Steganalysis Based on Embedding Probabilities of Pixels , 2016, IEEE Transactions on Information Forensics and Security.

[17]  Jiwu Huang,et al.  Adaptive steganalysis against WOW embedding algorithm , 2014, IH&MMSec '14.

[18]  Jessica J. Fridrich,et al.  Ensemble Classifiers for Steganalysis of Digital Media , 2012, IEEE Transactions on Information Forensics and Security.

[19]  Yun Q. Shi,et al.  An efficient JPEG steganographic scheme using uniform embedding , 2012, 2012 IEEE International Workshop on Information Forensics and Security (WIFS).