CAT：用于评估潜在扩散模型中保护性扰动鲁棒性的对比对抗训练
CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models
AI总结：
该研究揭示潜在扩散模型中保护性对抗样本的起效机制，提出基于轻量适配器的对比对抗训练CAT方法攻击防护手段，证明其鲁棒性不足，推动相关防护的改进。
摘要：
潜在扩散模型（Latent diffusion models）近期在众多下游图像合成任务中展现出卓越性能。然而，使用未授权数据定制潜在扩散模型会严重损害数据所有者的隐私与知识产权。作为保护性扰动的对抗样本已被开发，通过向定制样本引入难以察觉的噪声，阻止扩散模型有效学习这些样本，以此抵御未授权数据使用。本文通过定性与定量实验首次揭示，对抗样本在潜在扩散模型中作为保护性扰动有效的主要原因是其潜在表示发生了失真。随后我们提出了采用轻量级适配器的对比对抗训练（Contrastive Adversarial Training, CAT），作为针对这些防护方法的自适应攻击，凸显了其鲁棒性不足的问题。大量实验表明，我们的CAT方法显著降低了保护性扰动在定制任务中的有效性，敦促学界重新审视并提升现有保护性扰动的鲁棒性。代码可在https://github.com/senp98/CAT获取。
英文摘要：
Latent diffusion models have recently demonstrated superior capabilities in many downstream image synthesis tasks. However, customization of latent diffusion models using unauthorized data can severely compromise the privacy and intellectual property rights of data owners. Adversarial examples as protective perturbations have been developed to defend against unauthorized data usage by introducing imperceptible noise to customization samples, preventing diffusion models from effectively learning them. In this paper, we first reveal that the primary reason adversarial examples are effective as protective perturbations in latent diffusion models is the distortion of their latent representations, as demonstrated through qualitative and quantitative experiments. We then propose the Contrastive Adversarial Training (CAT) utilizing lightweight adapters as an adaptive attack against these protection methods, highlighting their lack of robustness. Extensive experiments demonstrate that our CAT method significantly reduces the effectiveness of protective perturbations in customization, urging the community to reconsider and improve the robustness of existing protective perturbations. The code is available at https://github.com/senp98/CAT.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
