Physical Evaluation of Naturalistic Adversarial Patches for Camera-Based Traffic-Sign Detection

• URL: http://arxiv.org/abs/2603.00217v1
• Date: Fri, 27 Feb 2026 16:54:53 GMT
• Title: Physical Evaluation of Naturalistic Adversarial Patches for Camera-Based Traffic-Sign Detection
• Authors: Brianna D'Urso, Tahmid Hasan Sakib, Syed Rafay Hasan, Terry N. Guo,
• Abstract summary: This paper studies how well Naturalistic Adversarial Patches (NAPs) transfer to a physical traffic sign setting when the detector is trained on a customized dataset for an autonomous vehicle (AV) environment.<n>We construct a composite dataset, CompGTSRB, by pasting traffic sign instances from the German Traffic Sign Recognition Benchmark onto undistorted backgrounds captured from the target platform.
• Score: 2.86989372262348
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper studies how well Naturalistic Adversarial Patches (NAPs) transfer to a physical traffic sign setting when the detector is trained on a customized dataset for an autonomous vehicle (AV) environment. We construct a composite dataset, CompGTSRB (which is customized dataset for AV environment), by pasting traffic sign instances from the German Traffic Sign Recognition Benchmark (GTSRB) onto undistorted backgrounds captured from the target platform. CompGTSRB is used to train a YOLOv5 model and generate patches using a Generative Adversarial Network (GAN) with latent space optimization, following existing NAP methods. We carried out a series of experiments on our Quanser QCar testbed utilizing the front CSI camera provided in QCar. Across configurations, NAPs reduce the detector's STOP class confidence. Different configurations include distance, patch sizes, and patch placement. These results along with a detailed step-by-step methodology indicate the utility of CompGTSRB dataset and the proposed systematic physical protocols for credible patch evaluation. The research further motivate researching the defenses that address localized patch corruption in embedded perception pipelines.

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