On Completeness of Feature Spaces in Blind Steganalysis

Blind steganalyzers can be used for many diverse applications in steganography that go well beyond a mere detection of stego content. A blind steganalyzer can also be used for constructing targeted attacks or as an oracle for designing steganographic methods. The feature space itself provides a low-dimensional model of covers useful for benchmarking. These applications require the feature space to be complete in the sense that the features fully characterize the space of covers. Incomplete feature sets may skew benchmarking scores and lead to poor steganalysis. As a simple test of completeness, we propose a general approach for constructing steganographic methods that approximately preserve the whole feature vector and thus become practically undetectable by any steganalyzer that uses the same feature set. We demonstrate the plausibility of this approach, which we call the Feature Correction Method (FCM) by constructing the FCM for a 274-dimensional feature set from a state-of-the-art blind steganalyzer for JPEG images.