High-Capacity Generative Image Steganography Approach for Hiding Multiple Secret Images

Current high-capacity image steganography methods face challenges in balancing hidden capacity, imperceptibility, and recovery quality. Existing embedding-based image-in-image steganography approaches tend to produce detectable artifacts when hiding multiple images, whereas existing generative methods struggle to conceal full-sized secret images and often generate unrealistic stego images. To address these issues, this paper proposes a novel generative steganography approach that hides multiple secret images in a single realistic generated image. Our main contributions include a meticulously designed autoencoder that compresses and injects secret images into the shallow layer of the generator to increase hidden capacity, a three-stage optimization strategy for stable training to enhance the recovery quality of secret images, and an automatic image selection procedure which explores the advantage of generation diversity to enhance the imperceptibility of stego images. Experimental results demonstrate that our method outperforms embedding-based approaches by achieving higher recovered image quality with a PSNR value of 30.45 dB when concealing four images while maintaining stronger resistance against steganalysis tools, with an accuracy of 50%. Against generative approaches, our method achieves a higher hidden capacity while preserving a superior visual quality of stego images, with a FID of 6.97, surpassing the suboptimal method's FID of 22.72.