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- W4313147817 abstract "The image-to-image translation tasks have been widely explored in recent years due to the rapid development of deep neural networks. Unfortunately, the misuse of image-to-image translation techniques may induce disastrous consequences. Although the existing proactive watermarking methods can give decent performances in tracing the manipulated images, they can be escaped by malicious users because they can only embed the watermarks after the generation of the deep manipulated images. Under such circumstances, we are motivated to propose a joint image-to-image translation and watermarking (JoinTW) framework with the correspondingly designed loss functions. Our JoinTW embeds the watermark as a fingerprint during the image-to-image translation process without being noticed or escaped, which enables the potential regulators to directly trace the producing source of the translated images once misuses happen. Experiments have demonstrated that our JoinTW can effectively embed watermarks without obvious degradations to the perceptual qualities of the generated images, as well as ensure the robustness of the watermark against various post-processing operations." @default.
- W4313147817 created "2023-01-06" @default.
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- W4313147817 date "2022-01-01" @default.
- W4313147817 modified "2023-09-30" @default.
- W4313147817 title "JoinTW: A Joint Image-to-Image Translation and Watermarking Method" @default.
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- W4313147817 doi "https://doi.org/10.1007/978-3-031-18913-5_58" @default.
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