FGRW: Fine-Grained Reversible Watermarking Based on Distribution-Adaptive Contrastive Augmentation Across Diverse Domains

Summary

The paper proposes FGRW, a fine-grained reversible watermarking scheme for protecting ownership of pre-trained models after cross-domain fine-tuning. It generates trigger and task samples using out-of-distribution awareness, and a representation-oriented regularization constraint selects modification spaces while the embedding loss guides the augmentation network. The authors report superior performance in fine-grained authorization and reversible integrity verification, and identify a trade-off between training a clean ideal model and obtaining a watermarked model.