Statistical Modeling of Footprints of QIM Steganography

Abstract

In this paper, a new model is proposed to analyze footprints of quantization (with and without message embedding). The proposed statistical model is used to develop a parametric steganalysis technique to attack quantization index modulation (QIM) steganography. The proposed scheme is based on the observations that message embedding using QIM introduce disturbance in the local-correlation in the cover-image. Presented steganalysis technique exploits rich spatial/temporal correlation in the natural images to estimate local-randomness in the test-image. The local-randomness, estimated from the test-image, is modeled using generalized Gamma distribution (GGD). A binary hypothesis test, based on generalized likelihood ratio test (GLRT), is used to detect the QIM-stego. Simulation results show that the proposed method can successfully distinguish between the quantized-cover and the QIM-stego with very low false alarm rates.

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