Nonparametric steganalysis of qim-based data hiding using kernel density estimation

This paper presents a novel steganalysis technique to at-tack quantization index modulation (QIM) steganography. Our method is based on the observation that QIM embed-ding disturbs neighborhood correlation in the transform do-main. We estimate the probability density function (pdf) of this statistical change in a systematic manner using a kernel density estimate (KDE) method. The estimated paramet-ric density model is then used for stego message detection. The impact of the choice of kernels on the estimated den-sity is investigated experimentally. Simulation results eval-uated 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 be-tween the quantized-cover and the QIM-stego with low false alarm rates (i.e. Pfn ≤ 0.03 and Pfp ≤ 0.19). We demon-strate that the proposed steganalysis scheme can successfully attack steganographic tools like Jsteg and JP Hide and Seek as well