Simulation of Physical Adversarial Attacks on Vehicle Detection Models
Se-Yoon Oh, Hunmin Yang · 2023
Physical adversarial attacks are a type of attack that aim to fool deep learning based object detectors by modifying the appearance of real-world objects or scenes. CG based simulation techniques are a type of method that use computer graphics to generate realistic adversarial examples that can be printed or projected onto physical objects or scenes. This technical research paper investigates physical adversarial attacks using synthetic image data and simulation based on computer graphics. Two application areas are explored, including object detection and physical adversarial attacks. The study presents successful attack results with an AP50 drop of 89%, demonstrating the vulnerability of object detection systems to physical adversarial attacks. The research utilizes synthetic image data and simulation to generate adversarial examples, providing a cost-effective and scalable method for testing the robustness of object detection systems. The findings of this study have important implications for improving the security and reliability of computer vision systems in real-world applications.