Non-uniform Quantization in Breaking HUGO

In breaking HUGO (Highly Undetectable Stegonagraphy), an advanced steganographic scheme recently developed for uncompressed images, the research on steganalysis has made rapid progress recently. That is, more advanced statistical models often utilizing high dimensional features have been adopted. It is noted that there is one thing in common for all of these newly developed advanced steganalytic schemes. That is, uniform quantization has been applied to residual images in order to reduce the feature dimensionality. In this paper, non-uniform quantization is proposed, developed and utilized to break the HUGO. In constructing non-uniform quantizers, a small portion of available samples from both cover and stego images are utilized to provide needed statistics. Utilizing non-uniform quantization we can achieve better steganalytic performance than using uniform quantization under, otherwise, the same framework.