mlsanityData to 7 Oct 2026

Evaluating Adversarial Attacks on Traffic Sign Classifiers beyond Standard Baselines

arXiv 2412.09150 ↗First posted 12 Dec 2024cs.CV cs.LGcited by 0

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Abstract (arXiv)

Adversarial attacks on traffic sign classification models were among the first successfully tried in the real world. Since then, the research in this area has been mainly restricted to repeating baseline models, such as LISA-CNN or GTSRB-CNN, and similar experiment settings, including white and black patches on traffic signs. In this work, we decouple model architectures from the datasets and evaluate on further generic models to make a fair comparison. Furthermore, we compare two attack settings, inconspicuous and visible, which are usually regarded without direct comparison. Our results show that standard baselines like LISA-CNN or GTSRB-CNN are significantly more susceptible than the generic ones. We, therefore, suggest evaluating new attacks on a broader spectrum of baselines in the future. Our code is available at urlhttps://github.com/KASTEL-MobilityLab/attacks-on-traffic-sign-recognition/.

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References · 23

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11 more
  1. 2019Fooling automated surveillance cameras: adversarial patches to attack person detectioncited by 268
  2. 2018ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detectorcited by 159
  3. 2022Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenoncited by 101
  4. 2017Adversarial Examples that Fool Detectorscited by 39
  5. 2021Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safetycited by 35
  6. 2022Feasibility of Inconspicuous GAN-generated Adversarial Patches against Object Detectioncited by 13
  7. 2023Adversarial Attacks on Traffic Sign Recognition: A Surveycited by 13
  8. 2020Targeted Physical-World Attention Attack on Deep Learning Models in Road Sign Recognitioncited by 7
  9. 2022Suppress with a Patch: Revisiting Universal Adversarial Patch Attacks against Object Detectioncited by 6
  10. 2023Physical Adversarial Attacks on Deep Neural Networks for Traffic Sign Recognition: A Feasibility Studycited by 5
  11. 2022Adversarial Vulnerability of Temporal Feature Networks for Object Detectioncited by 3

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