论文标题
批处理检测对抗示例 - 一种几何方法
Detecting Adversarial Examples in Batches -- a geometrical approach
论文作者
论文摘要
许多深度学习方法已成功地解决了计算机视觉和语音识别应用中的复杂任务。但是，已经发现这些模型的鲁棒性很容易受到扰动的输入或对抗性示例，这些示例对人眼是无法察觉的，但导致该模型做出了错误的输出决策。在这项研究中，我们适应并介绍了两个几何指标，密度和覆盖范围，并评估它们在未见数据批次中检测对抗样本中的使用。我们使用MNIST和两个来自MedMnist的现实生物医学数据集进行了经验研究这些指标，并受到了两种不同的对抗性攻击。我们的实验显示了两个指标检测对抗性例子的有希望的结果。我们认为，他的工作可以为这些指标在部署的机器学习系统中的使用而进一步研究，以监视对抗性示例或相关病理（例如数据集移动）可能攻击的攻击。
Many deep learning methods have successfully solved complex tasks in computer vision and speech recognition applications. Nonetheless, the robustness of these models has been found to be vulnerable to perturbed inputs or adversarial examples, which are imperceptible to the human eye, but lead the model to erroneous output decisions. In this study, we adapt and introduce two geometric metrics, density and coverage, and evaluate their use in detecting adversarial samples in batches of unseen data. We empirically study these metrics using MNIST and two real-world biomedical datasets from MedMNIST, subjected to two different adversarial attacks. Our experiments show promising results for both metrics to detect adversarial examples. We believe that his work can lay the ground for further study on these metrics' use in deployed machine learning systems to monitor for possible attacks by adversarial examples or related pathologies such as dataset shift.
