具有部分同位素的对抗性强度.
Loïc Shi-Garrier1, Nidhal Carla Bouaynaya2, Daniel Delahaye1
1ENAC, Université de Toulouse, 31400 Toulouse, France.
Entropy (Basel, Switzerland)
|February 23, 2024
概括
本研究引入了一个信息几何框架,以提高对抗性示例的深度学习模型的稳定性. 通过限制模型的部分同度性,它可以实现认证的稳定性,同时保持准确性.
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 (Deep Learning) 是一种深度学习.
背景情况:
- 深度学习模型表现出高性能,但缺乏稳定性保证,特别是在对抗性示例时.
- 这一漏洞损害了可信度,并限制了在需要认证强度的关键应用程序中的部署.
研究的目的:
- 在使用信息几何框架的多类分类中,为l2白盒攻击建立精确的稳定性标准.
- 通过在输入输出上推导标准来确保模型的稳定性.
主要方法:
- 赋予输出空间与费舍尔信息度量.
- 在输入-输出上推导了强度的雅可比安标准.
- 约束模型在训练点周围是部分同度的.
主要成果:
- 证明了部分同位素可以确保模型的稳定性.
- 对MNIST和CIFAR-10数据集的对抗对抗攻击的方法进行了评估.
- 在防御蒸和中介扰动的雅可比规则化方面取得了实质性的改进.
结论:
- 建议的信息几何框架为深度学习模型提供了经过认证的稳定性.
- 该方法为大扰动提供了比对手训练更高的稳定性,同时保持了准确性.
- 这种方法提高了深度学习在关键领域的可信度和部署性.
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