This supplementary content is mainly organized in the order of being referenced in the main manuscript. The architectures of the R networks are shown in Table 3. The training curve is shown in Figure 1. B.1 Where is the secret image encoded? Is every channel equally important?

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.

This supplementary content is mainly organized in the order of being referenced in the main manuscript. The architectures of the R networks are shown in Table 3. The training curve is shown in Figure 1. B.1 Where is the secret image encoded? Is every channel equally important?

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.