论文标题
深度神经网络可检测COVID-19病例的脆弱性，从胸部X射线图像到通用的对抗攻击
Vulnerability of deep neural networks for detecting COVID-19 cases from chest X-ray images to universal adversarial attacks
论文作者
论文摘要
在2019年新型冠状病毒疾病的流行病下，胸部X射线计算机断层扫描成像被用于有效筛查Covid-19患者。推进了基于深神经网络（DNN）的计算机辅助系统的开发，以迅速，准确地检测COVID-19病例，因为对数量有限的专家放射科医生的需求形成了筛选的瓶颈。但是，到目前为止，基于DNN的系统的脆弱性的评估很差，尽管DNN容易受到单个扰动的影响，称为通用对抗扰动（UAP），这可以在大多数分类任务中诱导DNN失败。因此，我们专注于代表性的DNN模型，用于检测胸部X射线图像的COVID-19病例，并评估其使用简单迭代算法生成的UAP的脆弱性。我们考虑非目标UAP，这会导致任务失败，导致输入分配不正确的标签，并靶向UAP，这会导致DNN将输入分类为特定类。结果表明，即使在小UAP的情况下，模型也容易受到非目标和靶向UAP的影响。特别是，对于非目标和目标攻击，图像数据集中图像数据集中的平均标准的2％范围> 85％和> 90％的成功率。由于非目标UAPS，DNN模型将大多数胸部X射线图像视为COVID-19案例。目标UAP使DNN模型将大多数胸部X射线图像分类为给定的目标类。结果表明，在DNN在COVID-19诊断中的实际应用中需要仔细考虑；他们特别强调需要解决安全问题的策略。例如，我们表明，使用UAPS对DNN模型的迭代微调可改善DNN模型对UAP的鲁棒性。
Under the epidemic of the novel coronavirus disease 2019 (COVID-19), chest X-ray computed tomography imaging is being used for effectively screening COVID-19 patients. The development of computer-aided systems based on deep neural networks (DNNs) has been advanced, to rapidly and accurately detect COVID-19 cases, because the need for expert radiologists, who are limited in number, forms a bottleneck for the screening. However, so far, the vulnerability of DNN-based systems has been poorly evaluated, although DNNs are vulnerable to a single perturbation, called universal adversarial perturbation (UAP), which can induce DNN failure in most classification tasks. Thus, we focus on representative DNN models for detecting COVID-19 cases from chest X-ray images and evaluate their vulnerability to UAPs generated using simple iterative algorithms. We consider nontargeted UAPs, which cause a task failure resulting in an input being assigned an incorrect label, and targeted UAPs, which cause the DNN to classify an input into a specific class. The results demonstrate that the models are vulnerable to nontargeted and targeted UAPs, even in case of small UAPs. In particular, 2% norm of the UPAs to the average norm of an image in the image dataset achieves >85% and >90% success rates for the nontargeted and targeted attacks, respectively. Due to the nontargeted UAPs, the DNN models judge most chest X-ray images as COVID-19 cases. The targeted UAPs make the DNN models classify most chest X-ray images into a given target class. The results indicate that careful consideration is required in practical applications of DNNs to COVID-19 diagnosis; in particular, they emphasize the need for strategies to address security concerns. As an example, we show that iterative fine-tuning of the DNN models using UAPs improves the robustness of the DNN models against UAPs.
