UDH: Universal Deep Hiding for Steganography, Watermarking, and Light Field Messaging

Neural networks have been shown effective in deep steganography for hiding a full image in another. However, the reason for its success remains not fully clear. Under the existing cover ( C) dependent deep hiding (DDH) pipeline, it is challenging to analyze how the secret ( S) image is encoded since the encoded message cannot be analyzed independently. We propose a novel universal deep hiding (UDH) meta-architecture to disentangle the encoding of S from C . We perform extensive analysis and demonstrate that the success of deep steganography can be attributed to a frequency discrepancy between C and the encoded secret image.