GoodDiffusion: Proactive Copyright Protection for Diffusion Bridge Models via Learnable Sample-specific Signatures
Abstract Overview
This paper proposes GoodDiffusion, a proactive copyright protection method for diffusion bridge models that embeds authorization into the generation process itself. Instead of relying on post-hoc watermarking or merely degrading outputs, the protected model generates high-quality results only for inputs carrying valid signatures and otherwise maps unauthorized requests to a predefined warning image. The authors first analyze a naive fixed-signature design and show it is vulnerable in white-box settings because a surrogate signature can be recovered by gradient-based optimization. To address this, they introduce a Learnable Signature Network that produces sample-specific, input-conditioned signatures, aiming to prevent a universal bypass signature from transferring across inputs.
Novelty
The main novelty is reframing backdoor-style mechanisms as a constructive, model-level access-control tool for copyright protection in diffusion bridge models. A second distinctive contribution is the use of learnable sample-specific signatures, rather than a single static trigger, to counter white-box signature recovery and non-transferably bind authorization to each input.
Results
Across CelebA and ImageNet image-to-image tasks including super-resolution, inpainting, and deblurring, GoodDiffusion blocks unauthorized use by producing warning outputs while preserving usable generation quality for authorized inputs. On CelebA, the reported abuse rate is reduced from 100% for unprotected models to near zero, with less than 0.06% in the summarized main cases, while error rates for authorized users remain low. Security experiments further show that recovered surrogate signatures can break the static-signature version, whereas the sample-specific design keeps surrogate generations poor and abuse rates at or near zero.
Key Points
- GoodDiffusion internalizes authorization into diffusion bridge generation, allowing signed inputs to generate target images and unsigned inputs to be redirected to a warning image.
- The paper theoretically and empirically shows that naive static signatures are fragile in white-box settings because adversaries can recover transferable surrogate signatures.
- The Learnable Signature Network generates input-conditioned signatures that improve security by preventing a universal recovered signature from generalizing across inputs.