Steganalyzer performances in operational contexts
This work addresses the challenge of detecting hidden information in images for security applications, but it appears incremental as it builds on existing steganalysis techniques without introducing a fundamentally new approach.
The paper investigates whether a universal steganalyzer can be built without knowledge of the steganography method, evaluating how changes in parameters or methods between learning and testing affect classification scores and exploring improvements by merging multiple methods during learning.
Steganography and steganalysis are two important branches of the information hiding field of research. Steganography methods consist in hiding information in such a way that the secret message is undetectable for the uninitiated. Steganalyzis encompasses all the techniques that attempt to detect the presence of such hidden information. This latter is usually designed by making classifiers able to separate innocent images from steganographied ones according to their differences on well-selected features. We wonder, in this article whether it is possible to construct a kind of universal steganalyzer without any knowledge regarding the steganographier side. The effects on the classification score of a modification of either parameters or methods between the learning and testing stages are then evaluated, while the possibility to improve the separation score by merging many methods during learning stage is deeper investigated.