Adversarial Attack On Yolov5 For Traffic And Road Sign Detection
Sanyam Jain

TL;DR
This study evaluates the vulnerability of YOLOv5 object detection to various adversarial attacks in traffic sign detection, revealing significant susceptibility and emphasizing the need for more robust models for safety-critical applications.
Contribution
It systematically investigates multiple adversarial attack methods on YOLOv5 for traffic sign detection, highlighting its vulnerabilities and the importance of developing more secure detection models.
Findings
YOLOv5 is vulnerable to several adversarial attacks.
Misclassification rates increase with attack strength.
Saliency maps help explain attack impacts.
Abstract
This paper implements and investigates popular adversarial attacks on the YOLOv5 Object Detection algorithm. The paper explores the vulnerability of the YOLOv5 to adversarial attacks in the context of traffic and road sign detection. The paper investigates the impact of different types of attacks, including the Limited memory Broyden Fletcher Goldfarb Shanno (L-BFGS), the Fast Gradient Sign Method (FGSM) attack, the Carlini and Wagner (C&W) attack, the Basic Iterative Method (BIM) attack, the Projected Gradient Descent (PGD) attack, One Pixel Attack, and the Universal Adversarial Perturbations attack on the accuracy of YOLOv5 in detecting traffic and road signs. The results show that YOLOv5 is susceptible to these attacks, with misclassification rates increasing as the magnitude of the perturbations increases. We also explain the results using saliency maps. The findings of this paper…
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Advanced Neural Network Applications · Anomaly Detection Techniques and Applications
