Statistical modeling and steganalysis of DFT-based image steganography

Ying Wang; Pierre Moulin

doi:10.1117/12.642357

15 February 2006 Statistical modeling and steganalysis of DFT-based image steganography

Ying Wang, Pierre Moulin

Proceedings Volume 6072, Security, Steganography, and Watermarking of Multimedia Contents VIII; 607202 (2006) https://doi.org/10.1117/12.642357
Event: Electronic Imaging 2006, 2006, San Jose, California, United States

Abstract

An accurate statistical model of cover images is essential to the success of both steganography and steganalysis. We study the statistics of the full-frame two-dimensional discrete Fourier transform (DFT) coefficients of natural images and show that the independently and identically distributed model with unit exponential distribution is not a sufficiently accurate description of the statistics of normalized image periodograms. Consequently, the stochastic quantization index modulation (QIM) algorithm that aims at preserving this model is detectable in principle. To discriminate the resulted stegoimages from cover images, we train a learning system on them. Building upon a state-of-the-art steganalysis method using the statistical moments of wavelet characteristic functions, we propose new features that are more sensitive to data embedding. The addition of these features significantly improves the steganalyzer's receiver operating characteristic (ROC) curve.

Citation Download Citation

Ying Wang and Pierre Moulin "Statistical modeling and steganalysis of DFT-based image steganography", Proc. SPIE 6072, Security, Steganography, and Watermarking of Multimedia Contents VIII, 607202 (15 February 2006); https://doi.org/10.1117/12.642357

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