Steganalysis of GIM-based data hiding using kernel density estimation

Authors:
Hafiz Malik;K. P. Subbalakshmi;Rajarathnam Chandramouli
Affiliations:
Stevens Institute of Technology, Hoboken, NJ;Stevens Institute of Technology, Hoboken, NJ;Stevens Institute of Technology, Hoboken, NJ
Venue:
Proceedings of the 9th workshop on Multimedia & security
Year:
2007

Citing 4
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Abstract

This paper presents a novel steganalysis technique to attack quantization index modulation (QIM) steganography. Our method is based on the observation that QIM embedding disturbs neighborhood correlation in the transform domain. We estimate the probability density function (pdf) of this statistical change in a systematic manner using a kernel density estimate (KDE) method. The estimated parametric density model is then used for stego message detection. The impact of the choice of kernels on the estimated density is investigated experimentally. Simulation results evaluated on a large dataset of 6000 quantized images indicate that the proposed method is reliable. The impact of the choice of message embedding parameters on the accuracy of the steganalysis detection is also evaluated. Simulation results show that the proposed method can distinguish between the quantized-cover and the QIM-stego with low false alarm rates (i.e. Pfn≤0.03 and Pfp≤0.19). We demonstrate that the proposed steganalysis scheme can successfully attack steganographic tools like Jsteg and JP Hide and Seek as well.