Elements of information theory
Elements of information theory
Introduction to data compression (2nd ed.)
Introduction to data compression (2nd ed.)
Informed Watermarking
Steganalysis of GIM-based data hiding using kernel density estimation
Proceedings of the 9th workshop on Multimedia & security
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
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This paper presents a nonparametric steganalysis technique to attack quantization index modulation (QIM) steganography and JSteg steganographic tool. The proposed scheme is based on the observation that message embedding using QIM introduces local irregularity (or randomness) in the cover-object. Presented steganalysis technique exploits rich spatial/temporal correlation in the multimedia-objects to estimate local irregularity in the test-object. The underlying density function based on local irregularity in the test-object is estimated in a systematic manner using a kernel density estimate (KDE) method. The Tsallis-divergence, a parametric divergence method, is used to quantify irregularity in the test-object. The Tsallis-divergence between the density function estimated from the test-object and its doubly-quantized version is used to distinguish between the cover and the stego. The impact of the choice of message embedding parameters such as quantization step-size, quality factor, etc. on the accuracy of the steganalysis detection for gray scale images is also evaluated. Simulation results presented for these message embedding parameters show that the proposed method can successfully distinguish between the quantized-cover and the QIM-stego with low false alarm rates. Detection performance of the proposed steganalysis scheme is also evaluated for JSteg steganographic tool.