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- W4300979854 abstract "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." @default.
- W4300979854 created "2022-10-04" @default.
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- W4300979854 date "2022-09-30" @default.
- W4300979854 modified "2023-10-10" @default.
- W4300979854 title "Generative Model Watermarking Based on Human Visual System" @default.
- W4300979854 doi "https://doi.org/10.48550/arxiv.2209.15268" @default.
- W4300979854 hasPublicationYear "2022" @default.
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