用图卷积网络检测CNN模型中的后门攻击，效果更准更快。
DeBUGCN -- Detecting Backdoors in CNNs Using Graph Convolutional Networks
- 将CNN权重转为图结构，用GCN做二分类判断是否含后门。
- 在MNIST、CIFAR-10和TrojAI数据集上检测准确率超95%。
- 首个用GCN检测后门的方案，对多种模型都有效，适合安全评估者。
深度神经网络（DNN）在关键应用中日益普及，其易受后门（木马）攻击的问题愈发突出。本文提出一种新型后门检测流程DeBUGCN，首次将图卷积网络（GCN）用于木马检测。利用DNN的静态权重构建层间图结构，再以GCN作为二分类器判断模型是否被感染。我们在MNIST手写数字和CIFAR-10图像数据集上训练了数百个干净与被注入后门的CNN模型，验证了DeBUGCN的有效性。针对真实场景，该方法在包含多种架构的TrojAI数据集上进行评估，展现了良好的鲁棒性和模型无关性。相比现有最先进算法，DeBUGCN在多个数据集上实现更高准确率且速度更快。
原文摘要 · Abstract (English)
Deep neural networks (DNNs) are becoming commonplace in critical applications, making their susceptibility to backdoor (trojan) attacks a significant problem. In this paper, we introduce a novel backdoor attack detection pipeline, detecting attacked models using graph convolution networks (DeBUGCN). To the best of our knowledge, ours is the first use of GCNs for trojan detection. We use the static weights of a DNN to create a graph structure of its layers. A GCN is then used as a binary classifier on these graphs, yielding a trojan or clean determination for the DNN. To demonstrate the efficacy of our pipeline, we train hundreds of clean and trojaned CNN models on the MNIST handwritten digits and CIFAR-10 image datasets, and show the DNN classification results using DeBUGCN. For a true In-the-Wild use case, our pipeline is evaluated on the TrojAI dataset which consists of various CNN architectures, thus showing the robustness and model-agnostic behaviour of DeBUGCN. Furthermore, on comparing our results on several datasets with state-of-the-art trojan detection algorithms, DeBUGCN is faster and more accurate.
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