Abstract: Traditional image steganography methods are susceptible to attack by steganalysis tools, whereas coverless image steganography method can essentially resist the attack of steganalyzers. However, most coverless image steganography algorithms suffer from problems such as low robustness, limited extraction accuracy, and poor imperceptibility. Therefore, this paper proposes a robust coverless steganography method for coding camouflage, which combines depth-based synthetic steganography with traditional clustering algorithms. The proposed algorithm matches the synthetic images generated by the coding network with similar images through perceptual hashing, and converts the transmitted images from synthetic images to real natural images to improve security. In addition, clustering algorithm is used to find the camouflage image which is corresponding to the similar image for transmission. The clustering is based on the convolutional neural networks (CNN) feature, which improves the ability to resist geometric attacks. Experimental analysis demonstrates that the proposed scheme achieves higher capacity and extraction accuracy, and solves the problems of low image quality and poor robustness of generative steganography schemes.

Key words: coverless image steganography, convolutional neural networks (CNN) clustering, perceptual hash, generate networks, camouflage image