High-Capacity Steganography Using Object Addition-Based Cover Enhancement for Secure Communication in Networks
Ruohan Meng, Qi Ming Cui, Zhili Zhou, Zhetao Li, Q. M. Jonathan Wu, Xingming Sun · IEEE Transactions on Network Science and Engineering · 2021
Steganography is an essential way to ensure secure communication in networks. Most steganographic algorithms imperceptibly embed secret information into an existing cover image. However, they generally cannot find a good trade-off between embedding capacity and security, as the existing covers available for users are usually far from optimal for embedding. To address this issue, instead of directly using the existing cover images, we propose a cover enhancement scheme for high-capacity image steganography, in which textured objects are generated and adaptively pasted to an existing cover based on the estimated embedding probability maps. Specifically, by estimating the embedding probability map of the cover image, we locate the high-embedding-cost region (HECR), which is inappropriate for embedding. Then, a textured object is generated by the conditional generative adversarial networks with the input of an affinely transformed object mask, and then is pasted to the located HECR for steganography. Since the image regions inappropriate for embedding are replaced by the textured object regions, the proposed scheme can provide a much higher embedding capacity for the state-of-the-art steganographic approaches. Extensive experiments demonstrate that the proposed scheme provides high embedding capacity,i.e., about 2.5 times higher than the state-of-the-art steganographic methods, and comparable anti-detectability to those methods.