Active steganalysis for interpolation-error based reversible data hiding

The interpolation-error based (IEB) reversible data hiding combines difference expansion and histogram-shifting techniques to embed secret data into interpolation-error histograms of a stego-image with high payload and low distortion has been proposed recently. In this paper, an active steganalysis scheme is proposed by analyzing and modeling histogram abnormality in the interpolation-error domain of sub-sampled images with the general Gaussian distribution (GGD) features. A support vector machine (SVM) classifier is trained by estimated parameters of GGD, and then a quantitative algorithm is presented to estimate the embedding length and locations. The experimental results show that the proposed active steganalysis scheme is effective in not only detecting the IEB method but also estimating its message length and embedding locations.

► The histogram abnormality of the IEB method is modeled with GGD.
► Compact features are analyzed and extracted for SVM classifier training.
► Adjacent histogram difference (AHD) is proposed to estimate embedding locations.
► Performance of the embedding length and locations estimation are outstanding.