深度神经网络中的对抗性强度基于随机集团模型的可变属性
Ruoxi Qin1, Linyuan Wang2, Xuehui Du1
1Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategy Support Force Information Engineering University, Zhengzhou, Henan, China.
Frontiers in neurorobotics
|August 24, 2023
概括
本研究介绍了一种随机组合策略,以保护深度神经网络 (DNN) 免受敌对攻击. 这种新的方法通过引入多样性和随机性来增强模型的稳定性,使白盒攻击更加困难.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 深度神经网络 (DNN) 容易受到敌对攻击,需要强大的防御机制.
- 当前的防御策略往往模仿网络安全方法,但可以是被动和反应性的.
- 敌对攻击利用漏洞,导致模型故障和安全风险.
研究的目的:
- 提出一个动态和随机组合策略,以提高DNN对抗敌对攻击的稳定性.
- 将多样性和随机性引入防御机制,以应对固定梯度漏洞.
- 提高整体模型对白盒攻击的弹性,降低可转移性.
主要方法:
- 定义集体数量,网络结构和光滑参数作为可变的集体属性.
- 开发了使用异质和冗余子模型的随机整体策略.
- 引入多样性和随机性来改变输入-输出梯度对应.
主要成果:
- 提出的方法在提高对抗性强度方面表现出有效性,特别是在针对非目标攻击方面.
- 对CIFAR10的实验结果显示,与传统组合模型相比,攻击成功率降低.
- 梯度的随机性质使得白盒攻击对攻击者来说更具挑战性.
结论:
- 随机组合策略提供了一个有希望的动态防御对DNN的对抗性攻击.
- 这种方法通过增加不可预测性和减少对基于梯度的攻击的脆弱性来提高模型安全性.
- 该方法有效地解决了在对抗场景中被动防御策略的局限性.
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