多样性增强的重建作为插件防御对抗对抗性干扰的防御
Zeshan Pang1,2, Xuehu Yan1,2, Shasha Guo1,2
1College of Electronic Engineering, National University of Defense Technology, Hefei, Anhui, China.
Frontiers in artificial intelligence
|October 13, 2025
概括
多样性增强重建 (DeR) 是一种新的插件防御对抗对立的例子. 它通过使用具有更好的多样性的频率感知重构器来提高稳定性,优于现有的方法.
科学领域:
- 人工智能的人工智能
- 机器学习安全 机器学习安全
- 计算机视觉 计算机视觉
背景情况:
- 深度学习模型容易受到对抗性示例的影响,在部署的系统中构成风险.
- 目前使用输入重建或随机转换的插件防御缺乏多样性,使其易受适应性攻击的影响.
研究的目的:
- 提出一种新的插件防御,多样性增强重建 (DeR),以提高对抗攻击的稳定性.
- 增强插件防御中的变化的多样性,以对抗适应性攻击策略.
主要方法:
- 开发了具有U-Net骨干和额外频率组件的频率意识重构器.
- 训练有素的重建人员使用DeR损失函数,同时优化重建和多样性目标.
- 实现了DeR作为一个能够产生异质梯度的插件防御.
主要成果:
- 在三个数据集和四个分类器架构上进行了广泛的实验,证明了DeR的卓越稳定性.
- 在严格的对抗环境下,DeR显著超过了最先进的插件防御.
- 评估证实了DeR在实时处理应用中的效率.
结论:
- 多样性增强的重建 (DeR) 提供了一个强大的和有效的防御对抗的例子.
- 拟议的方法通过引入增强的多样性,有效地解决了现有的插件防御的脆弱性.
- DeR提供了一个有前途的解决方案,用于保护大规模的深度学习服务免受复杂的对抗性攻击.
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