Cross-channel Image Steganography Based on Generative Adversarial Network

Bin Ma, Haocheng Wang, Yongjin Xian, Chunpeng Wang, Guanxu Zhao

Abstract: Traditional steganographic algorithms often suffer from issues such as low visual quality and limited resilience against steganalysis at high-capacity data embedding. To address these limitations, this paper proposes a cross-channel image steganography algorithm based on generative adversarial networks. In contrast to conventional image steganography techniques that directly embed secret data into original carrier images, the proposed algorithm embeds the secret data into the difference-plane of the two similar color channels. The proposed data embedding scheme involves a U-Net structure based generator for steganography, an adversarial network for steganalysis, and an optimization network for enhancing anti-steganalysis capabilities. In addition, a newly introduced Lion optimizer is introduced to effectively optimize the convergence speed of the proposed networks by adaptively setting learning rates and weight decay values. At the same time, the mean square error loss, structural similarity loss, and adversarial loss are employed to progressively enhance the visual quality of generated stego images. Consequently, a color image can be seamlessly embedded into the same-sized color image, and achieving high perceptual quality. Experimental results demonstrate that the proposed algorithm achieves a peak PSNR of 41.6 dB for color stego images, significantly reducing the distortion caused by secret image embedding.