Year: 2025
Type: conference-paper
Abstract: Physically simulated attack experiments were conducted using LED lights of different colors, the You Look Only Once (YOLO) v5 model, and the German Traffic Sign Recognition Benchmark (GTSRB) dataset. We attacked and interfered with the traffic sign detection model and tested the model’s recognition performance when it was interfered with by LED lights. The model’s accuracy in identifying objects was calculated with the interference. We conducted a series of experiments to test the interference effects of colored lighting. The attack with different colored lights caused a certain degree of interference to the machine learning model, which affected the self-driving vehicle’s ability to recognize traffic signs. It caused the self-driving system to fail to detect the existence of the traffic sign or commit recognition errors. To defend from this attack, we fed back the traffic signs into the training dataset and re-trained the machine learning model. This enabled the machine learning model to resist related attacks and avoid disturbance.
Authors: Chu-Hsing Lin, Chao-Ting Yu, Yan-Ling Chen
Institution: Tunghai University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4413889232