Nonparametric steganalysis of QIM data hiding using approximate entropy

This paper proposes a nonparametric steganalysis method for quantization index modulation (QIM) based steganography. The proposed steganalysis method uses irregularity (or randomness) in the test-image to distinguish between the coverand the stego-image. We have shown that plain-quantization (quantization without message embedding) induces regularity in the resulting quantized-image; whereas message embedding using QIM increases irregularity in the resulting QIM-stego image. Approximate entropy, an algorithmic entropy measure, is used to quantify irregularity in the test-image. Simulation results presented in this paper show that the proposed steganalysis technique can distinguish between the coverand the stego-image with low false rates (i.e. Pfp < 0.1 & Pfn < 0.07 for dither modulation stego and Pfp < 0.12 & Pfn < 0.002 for QIM-stego).