Further study on the security of S-UNIWARD

Recently, a new steganographic method was introduced that utilizes a universal distortion function called UNIWARD. The distortion between the cover and stego image is computed as a sum of relative changes of wavelet coefficients representing both images. As already pointed out in the original publication, the selection channel of the spatial version of UNIWARD (the version that hides messages in pixel values called S-UNIWARD) exhibits unusual properties – in highly textured and noisy regions the embedding probabilities form interleaved streaks of low and high embedding probability. While the authors of UNIWARD themselves hypothesized that such an artifact in the embedding probabilities may jeopardize its security, experiments with state-of-the-art rich models did not reveal any weaknesses. Using the fact that the cover embedding probabilities can be approximately estimated from the stego image, we introduce the novel concept of content-selective residuals and successfully attack S-UNIWARD. We also show that this attack, which is made possible by a faulty probabilistic selection channel, can be prevented by properly adjusting the stabilizing constant in the UNIWARD distortion function.

[1]  Jessica J. Fridrich,et al.  Design of adaptive steganographic schemes for digital images , 2011, Electronic Imaging.

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

[3]  Jessica J. Fridrich,et al.  Practical methods for minimizing embedding impact in steganography , 2007, Electronic Imaging.

[4]  Rainer Böhme,et al.  Advanced Statistical Steganalysis , 2010, Information Security and Cryptography.

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

[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.  Rich Models for Steganalysis of Digital Images , 2012, IEEE Transactions on Information Forensics and Security.

[8]  Jessica J. Fridrich,et al.  Steganalysis of JPEG images using rich models , 2012, Other Conferences.

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

[10]  Jessica J. Fridrich,et al.  Gibbs Construction in Steganography , 2010, IEEE Transactions on Information Forensics and Security.

[11]  Rainer Böhme,et al.  A Game-Theoretic Approach to Content-Adaptive Steganography , 2012, Information Hiding.

[12]  Fatih Kurugollu,et al.  A New Methodology in Steganalysis: Breaking Highly Undetectable Steganograpy (HUGO) , 2011, Information Hiding.

[13]  Vojtech Holub,et al.  On dangers of overtraining steganography to incomplete cover model , 2011, MM&Sec '11.

[14]  Tomás Pevný,et al.  Steganalysis by subtractive pixel adjacency matrix , 2010, IEEE Trans. Inf. Forensics Secur..

[15]  Gerhard Winkler,et al.  Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction , 2002 .

[16]  Rainer Bhme.  Advanced Statistical Steganalysis , 2010 .

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

[18]  Tomás Pevný,et al.  Using High-Dimensional Image Models to Perform Highly Undetectable Steganography , 2010, Information Hiding.

[19]  Rainer Böhme,et al.  Revisiting weighted stego-image steganalysis , 2008, Electronic Imaging.

[20]  Rainer Böhme,et al.  Weighted stego-image steganalysis for naive content-adaptive embedding , 2012, 2012 IEEE International Workshop on Information Forensics and Security (WIFS).