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- Flow-Based Robust Watermarking with Invertible Noise Layer for Black-Box Distortions95
Compared with the state-of-the-art architecture, the visual quality of the proposed framework improves by 2dB and the extraction accuracy after JPEG compression improves by more than 4%.
- De-END: Decoder-Driven Watermarking Network68
The results show that this framework outperforms the existing state-of-the-art (SOTA) END-based deep learning watermarking both in visual quality and robustness.
- DeNoL: A Few-Shot-Sample-Based Decoupling Noise Layer for Cross-channel Watermarking Robustness31
DeNoL is proposed, a decoupling noise layer for cross-channel simulation which only needs few-shot samples and can effectively simulate cross-Channel distortion with only 20 image pairs and assist in training a general and robust watermarking network.
- DERO: Diffusion-Model-Erasure Robust Watermarking9
A destruction and compensation noise layer (DCNL) is designed to approximate the distortion effects caused by latent diffusion model erasure (LDE), which is significantly improved from 75% with SOTA methods to an impressive 96% with DERO.
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