A Robust Coverless Image Steganography Based on an End-to-End Hash Generation Model

Recently, coverless steganography algorithms have attracted increased research attention due to their ability to completely resist steganalysis algorithms. However, the existing algorithms do not attain the same robust balance against geometric and non-geometric attacks. In addition, most of the existing methods need to transmit some auxiliary information along with the stego-images, which increases the cost of the hidden information. In this paper, a robust coverless image steganography algorithm based on a hash generation model is proposed. Different from the existing methods, the hash sequences are generated by an end-to-end CNN model, where the input is the original images, and the output is the corresponding hash sequences. Therefore, no auxiliary information needs to be transmitted when hiding the secret information. Moreover, the attention mechanism and adversarial training are introduced to improve the robustness of the model. The loss function is redesigned to accommodate these operations. Finally, an index structure is built to enhance the mapping efficiency. The experimental results show that the proposed method possesses better robustness and security compared with the state-of-the-art coverless image steganography algorithms.