PromptLA: Towards Integrity Verification of Black-box Text-to-Image Diffusion Models
Journal:
arXiv
Published Date:
Dec 20, 2024
Abstract
Despite the impressive synthesis quality of text-to-image (T2I) diffusion
models, their black-box deployment poses significant regulatory challenges:
Malicious actors can fine-tune these models to generate illegal content,
circumventing existing safeguards through parameter manipulation. Therefore, it
is essential to verify the integrity of T2I diffusion models. To this end,
considering the randomness within the outputs of generative models and the high
costs in interacting with them, we discern model tampering via the KL
divergence between the distributions of the features of generated images. We
propose a novel prompt selection algorithm based on learning automaton
(PromptLA) for efficient and accurate verification. Evaluations on four
advanced T2I models (e.g., SDXL, FLUX.1) demonstrate that our method achieves a
mean AUC of over 0.96 in integrity detection, exceeding baselines by more than
0.2, showcasing strong effectiveness and generalization. Additionally, our
approach achieves lower cost and is robust against image-level post-processing.
To the best of our knowledge, this paper is the first work addressing the
integrity verification of T2I diffusion models, which establishes quantifiable
standards for AI copyright litigation in practice.
