We conduct a brief yet comprehensive review of existing literature and outline three meta-architectures. Based on this, we summarize specific strategies for various applications of deep hiding, including steganography, light field messaging and watermarking. Finally, further insight into deep hiding is provided through incorporating the perspective of adversarial attack.
The booming interest in adversarial attacks stems from a misalignment between human vision and a deep neural network (DNN), i.e. a human imperceptible perturbation fools the DNN. A similar misalignment phenomenon has recently also been observed in the deep steganography task, where a decoder network can retrieve a secret image back from a slightly perturbed cover image. We attempt explaining the success of both in a unified manner from the Fourier perspective. Additionally, we propose two new variants of universal perturbations (1) Universal Secret Adversarial Perturbation; (2) high-pass UAP.
We propose a novel universal deep hiding (UDH) meta-architecture to disentangle the encoding of a secret image from the cover image. Our analysis demonstrates that the success of deep steganography can be attributed to a frequency discrepancy between the cover image and the encoded secret image. Exploiting UDHs universal property, we extend UDH for universal watermarking and light field messaging.