Anti-Inpainting: A Proactive Defense Approach against Malicious Diffusion-based Inpainters under Unknown Conditions

• URL: http://arxiv.org/abs/2505.13023v3
• Date: Sat, 02 Aug 2025 11:16:27 GMT
• Title: Anti-Inpainting: A Proactive Defense Approach against Malicious Diffusion-based Inpainters under Unknown Conditions
• Authors: Yimao Guo, Zuomin Qu, Wei Lu, Xiangyang Luo,
• Abstract summary: Anti-Inpainting is a proactive defense approach that achieves protection comprising three novel modules.<n>First, we introduce a multi-level deep feature extractor to obtain intricate features from the diffusion denoising process.<n>Second, we design a multi-scale, semantic-preserving data augmentation technique to enhance the transferability of adversarial perturbations.
• Score: 14.34509668877061
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the increasing prevalence of diffusion-based malicious image manipulation, existing proactive defense methods struggle to safeguard images against tampering under unknown conditions. To address this, we propose Anti-Inpainting, a proactive defense approach that achieves protection comprising three novel modules. First, we introduce a multi-level deep feature extractor to obtain intricate features from the diffusion denoising process, enhancing protective effectiveness. Second, we design a multi-scale, semantic-preserving data augmentation technique to enhance the transferability of adversarial perturbations across unknown conditions. Finally, we propose a selection-based distribution deviation optimization strategy to bolster protection against manipulations guided by diverse random seeds. Extensive experiments on InpaintGuardBench and CelebA-HQ demonstrate that Anti-Inpainting effectively defends against diffusion-based inpainters under unknown conditions. Additionally, our approach demonstrates robustness against various image purification methods and transferability across different diffusion model versions.

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