A NOVEL METHOD FOR FLEXIBLE CAPACITY MULTI-IMAGE HIDING

The noteworthiness of multiple image-hiding has increased in the current digital era. This owes to the growing demand for robust data protection and secure communication. The success of the multiple image hiding algorithm depends on concealing multiple secret images in one cover image and perfectly recovering all secret images at the receiving end. Unfortunately, the two primary challenges with current multi-image hiding methods are preserving high capacity while reducing visual distortions and guaranteeing precise recovery of every hidden image. The main challenge is to keep the hidden image visually obscured while concealing as much information as possible from a single cover image. In this paper, a novel framework for concealing multiple images using an invertible hiding neural network and image super-resolution is proposed. Extensive experiments on DIV2K, ImageNet, and COCO demonstrated that the proposed framework obtained significant results in terms of both imperceptibility and recovery accuracy of the secret image. Achieving an average of 27.16 dB, 26.65 dB, and 32.13 dB of PSNR and 0.810, 0.783, and 0.885 of SSIM for hiding four secret images with upscaling factor 2, respectively. The proposed framework successfully concealed and revealed up to 64 secret images. These results confirm the effectiveness of the proposed method in achieving both high invisibility and enabling multiple image concealment with a large capacity.