论文标题
通过战略性再培训在深神经网络中缓解后门缓解
Backdoor Mitigation in Deep Neural Networks via Strategic Retraining
论文作者
论文摘要
深度神经网络（DNN）在辅助和自动驾驶中变得越来越重要。使用使用机器学习获得的这些实体是不可避免的：不能使用传统的软件开发方法合理地开发诸如识别流量标志之类的任务。但是，DNN确实有一个问题，即它们主要是黑匣子，因此很难理解和调试。一个特别的问题是它们容易隐藏后门。这意味着DNN错误地分类了其输入，因为它考虑了不应决定输出的属性。恶意攻击者或不适当的培训可能会引入后门。无论如何，检测和去除它们在汽车区域很重要，因为它们可能会导致安全违规行为，并带来潜在的严重后果。在本文中，我们介绍了一种删除后门的新方法。我们的方法适用于故意和无意的后门。我们也不需要有关后门形状或分布的先验知识。实验证据表明，我们的方法在几个中型示例上表现良好。
Deep Neural Networks (DNN) are becoming increasingly more important in assisted and automated driving. Using such entities which are obtained using machine learning is inevitable: tasks such as recognizing traffic signs cannot be developed reasonably using traditional software development methods. DNN however do have the problem that they are mostly black boxes and therefore hard to understand and debug. One particular problem is that they are prone to hidden backdoors. This means that the DNN misclassifies its input, because it considers properties that should not be decisive for the output. Backdoors may either be introduced by malicious attackers or by inappropriate training. In any case, detecting and removing them is important in the automotive area, as they might lead to safety violations with potentially severe consequences. In this paper, we introduce a novel method to remove backdoors. Our method works for both intentional as well as unintentional backdoors. We also do not require prior knowledge about the shape or distribution of backdoors. Experimental evidence shows that our method performs well on several medium-sized examples.
