提出反修复防御框架，应对未知条件下的扩散模型篡改。
Anti-Inpainting: A Proactive Defense Approach against Malicious Diffusion-based Inpainters under Unknown Conditions
- 通过多级特征提取增强对扩散去噪过程的感知能力。
- 在未知条件下实现对多种修复模型的有效防护。
- 适合图像安全防护与对抗样本防御的研究者参考。
随着基于扩散模型的恶意图像篡改日益普遍，现有主动防御方法在未知条件下难以有效保护图像。为此，我们提出 Anti-Inpainting，一种包含三个创新模块的主动防御方法。首先，引入多级深度特征提取器，从扩散去噪过程中获取精细特征，提升防护效果。其次，设计多尺度、语义保持的数据增强技术，增强对抗扰动在未知条件下的迁移能力。最后，提出基于选择的分布偏移优化策略，强化对不同随机种子驱动篡改的抵御能力。在 InpaintGuardBench 与 CelebA-HQ 上的大量实验表明，Anti-Inpainting 能有效防御未知条件下的扩散式修复攻击。此外，该方法对多种图像净化手段具有鲁棒性，并可跨不同扩散模型版本实现迁移防护。
原文摘要 · Abstract (English)
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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