Abstract
As diffusion-based malicious image manipulation becomes increasingly prevalent, multiple proactive defense methods are developed to safeguard images against unauthorized tampering. However, most proactive defense methods only can safeguard images against manipulation under known conditions, and fail to protect images from manipulations guided by tampering conditions crafted by malicious users. To tackle this issue, we propose Anti-Inpainting, a proactive defense method that achieves adequate protection under unknown conditions through a triple mechanism to address this challenge. Specifically, a multi-level deep feature extractor is presented to obtain intricate features during the diffusion denoising process to improve protective effectiveness. We design multi-scale semantic-preserving data augmentation to enhance the transferability of adversarial perturbations across unknown conditions by multi-scale transformations while preserving semantic integrity. In addition, we propose a selection-based distribution deviation optimization strategy to improve the protection of adversarial perturbation against manipulation under diverse random seeds. Extensive experiments indicate the proactive defensive performance of Anti-Inpainting against diffusion-based inpainters guided by unknown conditions in InpaintGuardBench and CelebA-HQ. At the same time, we also demonstrate the proposed approach's robustness under various image purification methods and its transferability across different versions of diffusion models.
Abstract (translated)
随着基于扩散的恶意图像篡改变得越来越普遍，已经开发出多种主动防御方法来保护图像免受未经授权的修改。然而，大多数主动防御方法仅能在已知条件下保护图像不受操纵，并且无法防止由恶意用户定制篡改条件所导致的图像被操控。为了解决这一问题，我们提出了Anti-Inpainting，这是一种通过三重机制实现未知条件下充分保护的主动防御方法。 具体来说，我们提出了一种多级深度特征提取器，在扩散去噪过程中获取复杂的特征以提高防护效果。此外，我们设计了多层次语义保持数据增强技术，通过多种尺度变换增强了对抗性扰动在未知条件下的迁移能力，并同时保证了语义的完整性。另外，我们还提出了一种基于选择的分布偏差优化策略，以提升对不同随机种子条件下对抗性扰动防护的效果。 广泛的实验表明，Anti-Inpainting 在 InpaintGuardBench 和 CelebA-HQ 数据集上针对由未知条件引导的扩散式修复程序展示了积极防御性能。同时，我们还证明了所提出的方法在各种图像净化方法下具有鲁棒性，并且能够在不同的扩散模型版本之间实现迁移能力。 此研究工作对于增强基于人工智能技术的安全防护有着重要的贡献和应用价值。
URL
https://arxiv.org/abs/2505.13023
