GCL-MIH: A Generative-Based Coverless Multi-Image Hiding Method

Secure and high-capacity secret information transmission is an important task of the image hiding research. The existing image hiding methods face some critical issues: cover-based methods offer high capacity but introduce image distortion and security risks, whereas secure coverless methods have low capacity. To address these issues, this paper proposes a novel generative-based coverless multi-image hiding method called GCL-MIH, which can achieve high capacity and high security. The GCL-MIH first utilizes a feature reverse module to compress multiple secret images into multiple feature vectors and then normalizes them to generate a vector that conforms to a standard normal distribution, and finally inputs this vector into an invertible generative network (Flow-GAN) to generate a face image, enabling coverless multiple-image hiding without a predefined cover image. Experimental results demonstrate that the GCL-MIH successfully hides up to four images within a single generated face image, achieving a maximum embedding rate of 32 bpp. This capacity far exceeds those of the existing coverless methods. On the COCO test set, the generated stego images of the GCL-MIH are highly realistic (FID score: 11.98), and the recovered secret images exhibit satisfactory fidelity (the average PSNR and SSIM of four recovered secret images are 33.18 dB and 0.9412).