Camouflage generative adversarial network: a coverless full-image-to-image hiding

Image hiding, one of the most important data hiding techniques, is widely used to enhance cybersecurity when transmitting multimedia data. In recent years, deep learning-based image hiding algorithms have been designed to improve the embedding capacity whilst maintaining sufficient imperceptibility to malicious eavesdroppers. These methods can hide a full-size secret image into a cover image, thus allowing full-image-to-image hiding. However, these methods suffer from a trade-off challenge to balance the possibility of detection from the container image against the recovery quality of secret image. In this paper, we propose Camouflage Generative Adversarial Network (Cam-GAN), a novel two-stage coverless full-image-to-image hiding method named, to tackle this problem. Our method offers a hiding solution through image synthesis to avoid using a modified cover image as the image hiding container and thus enhancing both image hiding imperceptibility and recovery quality of secret images. Our experimental results demonstrate that Cam-GAN outperforms state-of-the-art full-image-to-image hiding algorithms on both aspects.