Evaluating the Impact of Adversarial Attacks on Traffic Sign Classification using the LISA Dataset
This addresses security risks in real-world traffic sign recognition systems, but it is incremental as it applies existing methods to a new dataset.
The paper tackled the vulnerability of traffic sign classifiers to adversarial attacks, showing that accuracy sharply declines with increased perturbation magnitude, e.g., using FGSM and PGD attacks on the LISA dataset.
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.