A Robust Coverless Steganography Scheme Using Camouflage Image

Recently, most coverless image steganography (CIS) methods are based on robust mapping rules. However, due to the limited mapping expression relationship between secret information and hash sequence, it is a challenge to further improve the hiding ability of coverless information hiding. Towards this goal, this paper proposes a robust coverless steganography scheme using camouflage image(CI-CIS). For the sender, CI-CIS introduces an camouflage image as the transmission carrier and establishes the correlation between them by Convolutional Neural Network(CNN) features. For the receiver, the camouflage image can retrieve the corresponding stego-image to recover the secret information. To this end, we designed a reversible retrieval scheme between stego-image and camouflage image by using image clustering. At the same time, since the semantic features represented by CNN are robust to image attacks, our method can increase the capability of the CIS effectively. Besides, we also build an inverted index to improve retrieval efficiency. Experimental results and analysis show that the CI-CIS has higher robustness and more flexible capacity setting compared with the existing CIS methods.

Automated summary

This paper proposes a robust coverless steganography scheme using camouflage image (CI-CIS), which introduces a camouflage image as the transmission carrier and establishes the correlation between them using Convolutional Neural Network (CNN) features. By designing a reversible retrieval scheme through image clustering, the camouflage image can retrieve the corresponding stego-image to recover the secret information. Experimental results show that CI-CIS has higher robustness and more flexible capacity setting compared to existing CIS methods.