  Intellectual property protection of deep neural networks is receiving
attention from more and more researchers, and the latest research applies model
watermarking to generative models for image processing. However, the existing
watermarking methods designed for generative models do not take into account
the effects of different channels of sample images on watermarking. As a
result, the watermarking performance is still limited. To tackle this problem,
in this paper, we first analyze the effects of embedding watermark information
on different channels. Then, based on the characteristics of human visual
system (HVS), we introduce two HVS-based generative model watermarking methods,
which are realized in RGB color space and YUV color space respectively. In RGB
color space, the watermark is embedded into the R and B channels based on the
fact that HVS is more sensitive to G channel. In YUV color space, the watermark
is embedded into the DCT domain of U and V channels based on the fact that HVS
is more sensitive to brightness changes. Experimental results demonstrate the
effectiveness of the proposed work, which improves the fidelity of the model to
be protected and has good universality compared with previous methods.
