Statistical modeling of footprints of QIM steganography

In this paper, a new model is proposed to characterize distortion due to message embedding. The proposed statistical model is used to develop a parametric steganalysis technique to attack quantization index modulation (QIM) steganography. We have shown that quantization with message embedding (a.k.a. QIM) introduces relatively stronger disturbance in the local-correlation in the test-image than quantization without message embedding. 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 image. Simulation results show that the proposed method can successfully distinguish between the quantized-cover and the QIM-stego with very low false alarm rates.