对混合经典-量子神经网络的后门攻击.
Ji Guo1, Wenbo Jiang2, Rui Zhang2
1Laboratory of Intelligent Collaborative Computing, University of Electronic Science and Technology of China, Chengdu, 611731, China.
概括
本研究引入了对混合古典量子神经网络 (HQNNs) 的后门攻击的首次系统分析. 结果显示,HQNNs比传统的卷积神经网络 (CNNs) 更强大地应对这些攻击.
科学领域:
- 量子机器学习就是量子机器学习
- 网络安全 网络安全
- 人工智能的人工智能
背景情况:
- 混合经典-量子神经网络 (HQNNs) 是量子机器学习 (QML) 的新兴领域.
- HQNN的安全漏洞,特别是后门攻击,仍然在很大程度上未被探索.
- 了解这些漏洞对于QML技术的安全部署至关重要.
研究的目的:
- 进行首次对针对HQNNs的后门攻击进行系统调查.
- 分析对HQNNs的后门攻击的理论基础,包括概括界限和扰乱要求.
- 为了比较HQNN对待后门攻击的强度,与传统的卷积神经网络 (CNN) 的强度进行比较.
主要方法:
- 开发一种新的框架,用于对HQNN的后门攻击.
- 对HQNN后门攻击的概括界限和最小扰动的理论分析.
- 在HQNN和CNN上实施和评估两种已建立的后门攻击方法.
- 引入"Qcolor"后门攻击,使用颜色转移作为触发器和NSGA-II进行超参数优化.
主要成果:
- 与CNN相比,HQNN对后门攻击具有更大的稳定性.
- 对HQNN的成功后门攻击需要比CNN更大的图像干扰.
- 拟议的Qcolor后门攻击证明了有效性,隐形性和强度,经过广泛的实验验证实了这一点.
- NSGA-II有效地优化了Qcolor后门攻击的超参数.
结论:
- 与当前的深度学习模型相比,HQNNs提供了针对某些类型的后门攻击的增强弹性.
- 在QML安全的背景下,Qcolor后门呈现了一个新的和强大的威胁载体.
- 进一步的研究是有必要的,以开发强大的防御机制,以抵御复杂的后门攻击HQNNs.
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