Abstract
Adversarial attacks pose significant threats to machine learning models by introducing carefully crafted perturbations that cause misclassification. While prior work has primarily focused on MNIST and similar datasets, this paper investigates the vulnerability of traffic sign classifiers using the LISA Traffic Sign dataset. We train a convolutional neural network to classify 47 different traffic signs and evaluate its robustness against Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) attacks. Our results show a sharp decline in classification accuracy as the perturbation magnitude increases, highlighting the models susceptibility to adversarial examples. This study lays the groundwork for future exploration into defense mechanisms tailored for real-world traffic sign recognition systems.
Abstract (translated)
对抗性攻击通过引入精心设计的扰动来误导机器学习模型，从而对这些模型构成重大威胁。虽然之前的研究主要集中在MNIST和类似的数据库上，但本文探讨了使用LISA交通标志数据集评估交通标志分类器的安全漏洞问题。我们训练了一个卷积神经网络来识别47种不同的交通标志，并用快速梯度符号方法（FGSM）和投影梯度下降法（PGD）攻击对其进行鲁棒性测试。我们的结果显示，随着扰动大小的增加，分类准确率急剧下降，这凸显了模型对于对抗样本的脆弱性。这项研究为未来开发针对现实世界交通标志识别系统的防御机制奠定了基础。
URL
https://arxiv.org/abs/2509.06835
