From Cover to Immucover: Adversarial Steganography via Immunized Cover Construction

Wanjie Li, Hongxia Wang, Yijing Chen

Abstract: Recent advancements in image steganography demonstrate that reasonable cover enhancement approaches can effectively improve the security performance of steganography. However, the existing proposals based on adversarial steganography against targeted steganalyzers are insufficient in terms of eliminating or reducing anomalous pixels caused by subsequent embedding. This limitation impedes the full potential of cover enhancement schemes for enhancing steganographic security. In this article, we present Immucover, a novel immunized cover image construction method that leverages fuzzy enhancement and an artificial immune system (AIS) to incorporate texture region- and edge-region-adaptive enhancement. Specifically, we first design a parameterized method to adaptively enhance the texture region of the given cover image using a distortion function. Then, Immucover detects and enhances the edge region of the cover image using a fuzzy-parameterized approach based on an optimized smallest univalue segment assimilating nucleus for edge detection. Finally, a powerful AIS module acts as an optimizer to optimize the parameters that affect the texture and edge area, i.e., the area where the secret information is suitably embedded. In this way, a so-called immunized cover image is generated. In addition, we develop a novel affinity metric to assess the antibody quality within the AIS module, which guides the generation of the immunized cover with higher security. Comprehensive experiments conducted on widely used datasets demonstrate that our Immucover provides significantly improved resistance to steganalysis and enhances the security of steganography.