关键词: 图像隐写；生成对抗网络；通道注意力机制；残差连接；自蒸馏

Abstract: Existing image steganography methods have made significant progress in concealment and antiattack capabilities, but still suffer from low image quality, limited information embedding capacity, and insufficient model stability and generalization ability. To address these issues, this paper proposes an image steganography model based on an improved GAN and selfdistillation (RCSDGAN). First, a residualchannel attention mechanism is designed and integrated into the DenseNet architecture to optimize its structure. Second, an encoding network and decoding network based on the enhanced DenseNet are constructed, combined with a discriminative network to form a complete image steganography framework. Finally, a selfdistillation training strategy is introduced. The selfdistillation loss is defined by calculating the difference between the teacher model’s output and the student model’s output from the same network, and this loss is incorporated into the overall loss function to enhance model stability and generalization capability. Experimental results demonstrate that, at an embedding rate of D=1bpp, the steganographic images achieve a PSNR of 50.4131dB, an SSIM of 0.9992, and an Accuracy of 99.96%.

Key words: image steganography; generative adversarial network; channel attention mechanism; residual connectivity; selfdistillation