Improving robust adaptive steganography via minimizing channel errors

Abstract

Online Social Networks (OSNs) are becoming increasingly entrenched in peoples lives and a huge number of images are shared on them every day, which are well-suited platforms for image steganography. Generally, the image can be regarded as the channel for steganography. However, OSNs usually perform lossy processing on uploaded images, which invalidates most of the current non-robust steganography algorithms. To solve the problem of poor detection resistance of existing robust steganography, we divide the causes of channel errors into two parts: steganography-related and steganography-independent. First, we propose a novel method to eliminate the effect of steganography-independent part. The message is embedded in the channel-processed cover which will modify some elements, and then the corresponding elements in the original image are replaced with the modified elements to generate the stego for transmission. Then for the steganography-related part, the wet paper model is employed to minimize the channel error rate. The proposed algorithm can not only resist JPEG recompression but also enhancement filtering which was not considered in previous algorithms. Experimental results show that the proposed algorithm surpasses previous methods in terms of both robustness and security by a clear margin. Besides, take the example of one of the most complex platforms, Facebook, this method can achieve error-free steganography without error correction codes.