  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 have important
implications for the safety and reliability of object detection algorithms used
in traffic and transportation systems, highlighting the need for more robust
and secure models to ensure their effectiveness in real-world applications.
