Invisible image watermarks are commonly evaluated against benign postprocessing operations such as compression, resizing, blur, and color changes. These tests leave out a different threat: a learned remover that preserves semantic image content while discarding residual evidence that carries the payload. We propose Dis...
Qi Li, Ji-Dong Yang, Feng-Lei Fan et al.· 0 citations
Existing invisible watermark removal methods often struggle to accurately capture the watermark-bearing features, leading to an unfavorable trade-off between watermark suppression and perceptual fidelity. In this paper, we propose the Frequency-Decoupled Diffusion Watermark Attack Network (FDDWAN), a coarse-to-fine fra...
Chun-peng Wang, Yuxin Li, Xiaoyu Wang et al.· arXiv.org· 0 citations
Existing watermark attacks typically rely on predefined signal-processing operations or locally constrained restoration networks, making it difficult to capture the long-range dependencies of globally distributed watermark signals and resulting in an unfavorable trade-off between removal effectiveness and visual fideli...
Chun-peng Wang, Yan Shi, Zhi-qiu Xia et al.· arXiv.org· 0 citations
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