通过度量学习实现多模式数据的对抗性稳定性
Sarwar Khan1,2,3, Jun-Cheng Chen1,2, Wen-Hung Liao2,3
1Research Center for Information Technology Innovation, Academia Sinica, Taipei 11529, Taiwan.
Sensors (Basel, Switzerland)
|July 14, 2023
概括
本研究介绍了一种多原型的度量学习规范化,以改善深度神经网络的对抗性训练防御. 这种新的方法提高了对抗对手攻击的稳定性,而无需额外的计算成本.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 深度神经网络面临来自对抗性攻击的重大安全威胁.
- 现有的对抗防御方法在现实世界多模式数据集中经常失败,原因是单模式聚焦.
- 目前的对抗性训练方法很难捕获全面的数据表示,以进行强大的防御.
研究的目的:
- 为对抗性培训提出一种新的多原型度量学习规范化方案.
- 为了增强对手训练对复杂攻击的防御能力.
- 提高深度神经网络在复杂,多模式数据设置中的弹性.
主要方法:
- 开发了一种多原型的度量学习规范化技术.
- 将这种规范化整合到对抗性培训框架中.
- 在各种数据集上进行了广泛的实验,包括CIFAR10,CIFAR100,MNIST和Tiny ImageNet.
主要成果:
- 提出的方法显著提高了最先进的对抗训练技术的性能.
- 与现有方法相比,在多原型数据集 (CIFAR10,CIFAR100) 上实现了优越的防御性能.
- 通过防止对抗性示例潜伏表征的重大变化,证明了增强的稳定性.
结论:
- 多原型度量学习规范化是对抗训练的有效增强.
- 该方法提供了改进的防御能力,而不会增加计算开销.
- 这种方法代表了防御深度神经网络免受敌对攻击的重大进步,特别是在多式联络场景中.
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