High-volume data hiding in images: Introducing perceptual criteria into quantization based embedding

Abstract

Information-theoretic analyses for data hiding prescribe embedding the hidden data in the choice of quantizer for the host data. In this paper, we consider a suboptimal implementation of this prescription, with a view to hiding high volumes of data in images with low perceptual degradation. Our two main findings are as follows: (a) In order to limit perceptual distortion while hiding large amounts of data, the hiding scheme must use perceptual criteria in addition to information-theoretic guidelines. (b) By focusing on “benign” JPEG compression attacks, we are able to attain very high volumes of embedded data, comparable to information-theoretic capacity estimates for the more malicious Additive White Gaussian Noise (AWGN) attack channel, using relatively simple embedding techniques.