Constructing Immune-Cover for Improving Holistic Security of Spatial Adaptive Steganography

The cover image with strong resistance against embedding distortion has promise for improving the holistic security of steganography. However, existing methods use the vulnerability of Convolutional Neural Network (CNN) to construct the enhanced cover which can only deceive the target CNN-based steganalyzer, but is not more suitable for steganography. When resisting steganalysis outside the target steganalyzer, its performance drops significantly. In this paper, we propose an immune-cover construction scheme via Artificial Immune System (AIS). By the association between the steganography and immune theory, we regard the cover as the organism, the distortion introduced by steganography as the pathogenic factor, and the immunoprocessing for optimizing the original cover as the antibody. Based on AIS, the optimal immunoprocessing is dynamically searched and performed on the original cover to construct an immune-cover which is most suitable for steganography. Besides, the proposed method carefully selects the immunoprocessing region to prevent artifacts, and guarantees the visual quality of the immune-cover through the constraint of the immunoprocessing intensity. Extensive experimental results demonstrate that the proposed immune-cover has much stronger resistance against embedding distortion compared with the related methods, thus significantly improving the holistic security of the adaptive steganography evaluated on both traditional and CNN-based steganalyzers.