Imperceptible Protection against Style Imitation from Diffusion Models
To address copyright infringement and artistic style appropriation risks posed by diffusion models, this paper proposes a visually lossless copyright protection method. The approach comprises three key contributions: (1) perception-sensitive map-guided instance-aware fine-tuning, enabling fine-grained stylistic perturbation; (2) difficulty-aware dynamic intensity modulation, which adaptively adjusts perturbation magnitude based on the sample’s stylistic mimicability; and (3) a multi-scale perceptual constraint library, jointly optimizing defense robustness and image fidelity. Without introducing perceptible visual artifacts, the method achieves over 92% style imitation suppression, reduces LPIPS by 41%, and improves FID by 27%, significantly outperforming existing state-of-the-art methods.