对抗性导向扩散模型用于对抗性净化
Guang Lin1, Zerui Tao2, Jianhai Zhang3
1Department of Electronic and Information Engineering, Tokyo University of Agriculture and Technology, 184-8588, Tokyo, Japan; RIKEN Center for Advanced Intelligence Project (AIP), 103-0027, Tokyo, Japan.
概括
本研究介绍了一种对抗指导扩散模型,以改善对抗净化 (AP) 防御. 这种新的方法通过保留语义信息来提高稳定性,同时有效地消除对抗性干扰.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 扩散模型 (DM) 为消除威胁提供了强大的对抗性净化 (AP).
- 现有的方法可能会损害语义信息或保留扰动.
- 导向扩散技术与可以保留扰动的距离测量作斗争.
研究的目的:
- 开发一种新的对抗导向扩散模型,用于强大的对抗净化.
- 在干扰清除过程中增强语义信息的保存.
- 为了提高基于扩散模型的对抗性净化的整体稳定性.
主要方法:
- 提出了一个具有新的对抗指导机制的对抗指导扩散模型.
- 利用一个辅助神经网络,对指导进行敌对训练.
- 使用隐藏表示的距离尺度,而不是像素级值,作为指导.
主要成果:
- 在同时维护语义信息和消除对抗性干扰方面表现出有效性.
- 在现有的基于DM的AP方法的稳定性方面取得了显著的改进.
- 在CIFAR-10上报告了高达7.30%的平均强大的精度改进.
结论:
- 提出的对抗性指导有效地平衡了语义维护和扰乱删除.
- 该方法显著提高了基于扩散模型的对抗性净化性能.
- 这种方法为加强深度学习模型对抗对方攻击的安全性提供了一个有希望的方向.
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