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 cover- and 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 cover- and 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).
This paper presents a novel scheme for detection of watermarks embedded in multimedia signals using spread spectrum (SS) techniques. The detection method is centered on using the model that the embedded watermark and the host signal are mutually independent. The proposed detector assumes that the host signal and the watermark obey non-Gaussian distributions. The proposed blind watermark detector employs underdetermined blind source separation (BSS) based on independent component analysis (ICA) for watermark estimation from the watermarked image. The mean-field theory based undetermined BSS scheme is used for watermark estimation. Analytical results are presented showing that the proposed detector performs significantly better than the existing correlation based blind detectors traditionally used for SS-based image watermarking.