Feature restoration and distortion metrics

Our work focuses on Feature Restoration (FR), a technique which may be used in conjunction with steganographic schemes to reduce the likelihood of detection by a steganalyzer. This is done by selectively modifying the stego image to reduce a given distortion metric to a chosen target feature vector. The technique is independent of the exact steganographic algorithm used and can be applied with respect to any set of steganalytic features and any distortion metric. The general FR problem is NP-complete and hence intractable, but randomized algorithms are able to achieve good approximations. However, the choice of distortion metric is crucial: our results demonstrate that, for a poorly chosen metric or target, reducing the distortion frequently leads to an increased likelihood of detection. This has implications for other distortion-reduction schemes.