论文标题
通过在深网的重量上应用矩阵分解，在计算机视觉中检测后门攻击检测
Backdoor Attack Detection in Computer Vision by Applying Matrix Factorization on the Weights of Deep Networks
论文作者
论文摘要
深度神经网络（DNNS）和云服务对培训他们的重要性越来越重要，这意味着不良演员有更多的激励和机会插入后门以改变训练有素的模型的行为。在本文中，我们介绍了一种新型的后门检测方法，该方法使用独立矢量分析（IVA）从预训练的DNN的权重中提取特征，然后是机器学习分类器。与其他检测技术相比，这具有许多好处，例如不需要任何培训数据，适用于跨域，使用广泛的网络架构运行，而不是假设用于改变网络行为的触发器的性质，并且具有高度可扩展性。我们讨论检测管道，然后在两个计算机视觉数据集上展示有关图像分类和对象检测的结果。我们的方法在效率方面优于竞争算法，并且更准确，有助于确保深入学习和AI的安全应用。
The increasing importance of both deep neural networks (DNNs) and cloud services for training them means that bad actors have more incentive and opportunity to insert backdoors to alter the behavior of trained models. In this paper, we introduce a novel method for backdoor detection that extracts features from pre-trained DNN's weights using independent vector analysis (IVA) followed by a machine learning classifier. In comparison to other detection techniques, this has a number of benefits, such as not requiring any training data, being applicable across domains, operating with a wide range of network architectures, not assuming the nature of the triggers used to change network behavior, and being highly scalable. We discuss the detection pipeline, and then demonstrate the results on two computer vision datasets regarding image classification and object detection. Our method outperforms the competing algorithms in terms of efficiency and is more accurate, helping to ensure the safe application of deep learning and AI.
