ADVERSARIAL ATTACKS AND DEFENSE MECHANISMS IN AUTONOMOUS VEHICLES
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The integration of deep learning (DL) and artificial intelligence (AI) has significantly advanced the capabilities of autonomous vehicles (AVs), enabling intelligent perception, planning, and decision-making. However, these enhancements have also introduced new cybersecurity vulnerabilities, particularly in the form of adversarial attacks. This review focuses on four key adversarial attack types—Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), physical adversarial patch attacks, and sensor spoofing—that pose serious threats to AV safety. It further explores advanced and practical defense mechanisms such as hybrid deep learning models, patch-based occlusion-aware detection, autoencoders with memory modules, and sensor fusion-based spoofing detection frameworks. The review is supported by recent research papers and highlights both theoretical and applied perspectives, covering classification and perception tasks, sensor vulnerabilities, and real-world validation. Through detailed analysis of attack mechanisms, impacts, and mitigation strategies, the urgent need for scalable, real-time, and integrated defense systems threats is emphasized to secure AI-driven transportation from evolving adversarial.
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Luiz, A. A. (2026). Adversarial Attacks and Defense Mechanisms in Autonomous Vehicles. International Journal of Science, Strategic Management and Technology, 02(05). https://doi.org/10.55041/ijsmt.v2i5.125
Luiz, Antony. "Adversarial Attacks and Defense Mechanisms in Autonomous Vehicles." International Journal of Science, Strategic Management and Technology, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i5.125.
Luiz, Antony. "Adversarial Attacks and Defense Mechanisms in Autonomous Vehicles." International Journal of Science, Strategic Management and Technology 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i5.125.
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