An Image Robust Batch Steganography Framework With Minimum Embedding Signs

Yi Zhang, Yuanyuan Ma, Qianqian Zhang, Yang Pei, Xiangyang Luo

Abstract: The flourishing online social networks provide natural and ideal channels for covert communication, especially image batch steganography, which is characterized by high capacity and efficiency. To address the challenges of covert and reliable messaging, an image robust batch steganography framework (Multi-Stega) is proposed. Utilizing separable steganalysis feature selection and difference measurement, Multi-Stega first designs an embedding sign function to describe steganographic distortion, aiming directly at improving resistance against steganalysis. Then, Multi-Stega applies a cover selection algorithm based on steganographic fitness and a payload distribution strategy based on multi-stage decision optimization, to realize message allocation with minimum embedding signs. On this basis, Multi-Stega can employ any image robust steganography algorithm and universal joint source-channel code to facilitate message embedding and extraction. To analyze its validity, instances are implemented and compared with some state-of-the-art algorithms. Experimental results demonstrate that the separable feature selection provides strong support for precise embedding signs measurement, and Multi-Stega can enhance the detection resistance of representative robust steganography algorithms by 35.10% on average. Covert communication tests on Facebook and Weibo also indicate the concealment and reliability of Multi-Stega, which shows the prospect of practical applications.

External IDs:dblp:journals/tifs/ZhangMZPL25