变异对抗防御:对抗训练的贝叶斯观点
IEEE transactions on pattern analysis and machine intelligence
|December 13, 2023
概括
本研究介绍了变异对抗防御,这是一种改进对抗攻击防御的新方法. 它通过考虑对抗性样本分布来增强模型性能来增强培训.
科学领域:
- 计算机科学 计算机科学
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 现有的对抗防御方法缺乏理论保证,导致过和梯度掩盖等问题.
- 以点为准的对抗性抽样提供了不足的支持,阻碍了强大的决策边界的形成.
研究的目的:
- 从理论上分析强大的准确性和训练集复杂性之间的关系.
- 为增强防御能力提出一种新的,分布式的对抗性训练方案.
主要方法:
- 强大的准确性和训练集复杂性的理论分析.
- 发展变量对抗防御 (VAD) 用于分布智能的对抗样本生成.
- 泰勒扩展技术用于分析拟议方法的可解释性.
主要成果:
- 变异对抗防御将防御从点智能升级到分布智能抽样.
- 该方法扩大了对抗数据的支持区域,提高了培训的稳定性.
- 通过增加一个更大的支持区域来增加培训集,可以提高决策边界的平滑性.
结论:
- 变异对抗防御提供了一个理论上有基础的方法来增强对抗的强度.
- 分布式策略有效地解决了点式抽样的局限性.
- 该方法提供了对抗攻击的更易解释和更强大的防御.
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