Generative Model Watermarking Based on Human Visual System
This work addresses intellectual property protection for generative models, offering incremental improvements in watermarking techniques for image processing applications.
The paper tackles the problem of limited watermarking performance in generative models for image processing by analyzing channel effects and introducing two watermarking methods based on the human visual system (HVS) in RGB and YUV color spaces, resulting in improved fidelity and universality compared to previous methods.
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.