AdvPainting: Clean-text Jailbreaking Against Inpainting Models

摘要

Text-guided inpainting models are widely used for image editing, restoration, and content generation due to their ability to produce high-fidelity results aligned with natural language prompts. However, these models remain vulnerable to jailbreaking attacks, where adversaries manipulate inputs to generate pornographic or violent content. While prior attacks rely on adversarial text prompts, they are increasingly mitigated by advanced text-based safety filters and manual review. In this work, we propose a new attack paradigm that bypasses these defenses by leveraging the image modality alone. Specifically, we inject imperceptible adversarial perturbations into the input image, enabling successful jailbreaks even when paired with clean prompts (e.g., ''a woman''). To achieve this, we address two key challenges: (1) stabilizing the optimization of adversarial perturbations via a novel gradient estimator, and (2) ensuring visual imperceptibility through a diffusion-based perturbation generator. Extensive experiments show that our method successfully compromises the Stable Diffusion Inpainting model-despite its built-in image and text safety checkers-achieving an average attack success rate (ASR) of 85.7%, significantly outperforming baselines (58.7%). Moreover, our attack exhibits strong transferability across models and maintains robustness against common image pre-processing defenses. Warning: Blurred or masked NSFW imagery is contained.