The majority of steganographic utilities for the camouflage of confidential communication suffers from fundamental weaknesses. On the way to more secure steganographic algorithms, the development of attacks is essential to assess security. We present both visual attacks, making use of the ability of humans to clearly discern between noise and visual patterns, and statistical attacks which are much easier to automate.
The visual attacks presented here exemplify that at least EzStego v2.0b3, Jsteg v4, Steganos v1.5, and S-Tools v4.0 suffer from the misassumption that least significant bits of image data are uncorrelated noise. Beyond that, this paper introduces more objective methods to detect steganography by statistical means.