arXiv 2509.068352025-09-08cs.CV

评估对抗攻击对使用LISA数据集的交通标志分类的影响

Evaluating the Impact of Adversarial Attacks on Traffic Sign Classification using the LISA Dataset

Department of Computer Engineering Benedict College Columbia, SC, USA（计算机工程系布恩迪克学院美国哥伦比亚州）；Department of Computer Science Benedict College Columbia, SC, USA（计算机科学系布恩迪克学院美国哥伦比亚州）；Department of Civil Engineering Clemson University Clemson, SC, USA（土木工程系哥伦比亚大学美国哥伦布州）

Nabeyou Tadessa, Balaji Iyangar, Mashrur Chowdhury

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AI总结：

本文使用LISA数据集训练CNN分类47种交通标志，评估FGSM和PGD攻击的影响，发现扰动增大导致准确率急剧下降，为真实世界防御机制研究奠定基础。

摘要：

对抗攻击通过引入精心设计的扰动导致模型误分类，对机器学习模型构成重大威胁。虽然先前的工作主要集中于MNIST及类似数据集，本文使用LISA交通标志数据集研究交通标志分类器的脆弱性。我们训练了一个卷积神经网络来分类47种不同的交通标志，并评估其对抗快速梯度符号方法（FGSM）和投影梯度下降（PGD）攻击的鲁棒性。我们的结果显示，随着扰动幅度的增加，分类准确率急剧下降，凸显了模型对对抗样本的敏感性。本研究为未来探索针对真实世界交通标志识别系统的防御机制奠定了基础。

英文摘要：

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.

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