通过概率训练实现内在的对抗性强度
概括
逆向概率训练 (APT) 通过建模逆向分布来增强深度学习安全性,弥合自然和逆向示例之间的差距. 这种方法通过解决当前对抗训练方法中的优化偏差来提高对攻击的稳定性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 深度学习安全 深度学习安全
背景情况:
- 深度神经网络 (DNN) 容易受到对抗性干扰,从而构成安全风险.
- 当前的对抗性训练方法优化了自然例子,忽略了对抗性领域对手,导致决策边界低于最佳并降低了稳定性.
研究的目的:
- 提出对抗概率训练 (APT) 以提高DNN的稳定性.
- 通过建模潜在的对抗分布,弥合自然和对抗例子之间的分布差距.
主要方法:
- 在效率的特征水平上估计对抗分布参数.
- 使用对抗性概率模型和原始对抗性示例进行脱分布对齐.
- 实施一种新的重量机制,考虑到对抗力和领域不确定性.
主要成果:
- APT表现出对各种对抗性攻击的卓越性能.
- 该方法在不同的数据集和场景中显示出有效性.
- 拟议的方法减轻了优化偏差的负面影响.
结论:
- 对抗概率训练 (APT) 提供了一种更有效的策略,用于提高DNN中的对抗稳定性.
- 功能级估计和新型重权重机制有助于增强对复杂攻击的安全性.
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