Hiding multiple images into a single image via joint compressive autoencoders

Interest in image hiding has been continually growing. Recently, deep learningbased image hiding approaches improve the hidden capacity significantly. However, the major challenges of the existing methods are that they are difficult to balance between the errors of the modified cover image and those of the recovered secret image. To solve this problem, in this paper, we develop an image hiding algorithm based on a joint compressive autoencoder framework. Further, we propose a novel strategy to enlarge the hidden capacity, i.e., hiding multi-images in one container image. Specifically, our approach provides an extremely high image hidden capacity coupled with small reconstruction errors of the secret image. More importantly, we tackle the trade-off problem of earlier approaches by mapping the image representations in the latent spaces of the joint compressive autoencoder models, leading to both high visual quality of the container image and low reconstruction error the secret image. In an extensive set of experiments, we confirm our proposed approach to outperform several state-of-the-art image hiding methods, yielding high imperceptibility and steganalysis resistance of the container images with high recovery quality of the secret images, while improving the image hidden capacity significantly (four times higher than full-image hiding capacity).