论文标题
用于后门神经网络的一种异常检测方法：面部识别作为案例研究
An anomaly detection approach for backdoored neural networks: face recognition as a case study
论文作者
论文摘要
后门攻击允许攻击者嵌入功能危害任何算法，机器学习与否的适当行为。这种隐藏的功能可能一直无效，用于正常使用该算法，直到被攻击者激活为止。考虑到隐形后门攻击的方式，如果要在像边境或访问控制等关键的应用程序中部署此类网络，这些后门的后果可能会造成灾难性。在本文中，我们根据异常检测原理提出了一种新颖的后dored网络检测方法，涉及访问训练数据的清洁部分和训练有素的网络。我们在考虑各种触发器，位置和身份对时强调了它的潜力，而无需对后门及其设置的性质做出任何假设。我们在一个新颖的后式网络数据集上测试我们的方法，并报告得分完美的可检测性结果。
Backdoor attacks allow an attacker to embed functionality jeopardizing proper behavior of any algorithm, machine learning or not. This hidden functionality can remain inactive for normal use of the algorithm until activated by the attacker. Given how stealthy backdoor attacks are, consequences of these backdoors could be disastrous if such networks were to be deployed for applications as critical as border or access control. In this paper, we propose a novel backdoored network detection method based on the principle of anomaly detection, involving access to the clean part of the training data and the trained network. We highlight its promising potential when considering various triggers, locations and identity pairs, without the need to make any assumptions on the nature of the backdoor and its setup. We test our method on a novel dataset of backdoored networks and report detectability results with perfect scores.
