Adversarial Attacks and Countermeasures on Image Classification-based Deep Learning Models in Autonomous Driving Systems: A Systematic Review

Engineering notes

Key topics: autonomous driving system, autonomous driving, real-world driving. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

The rapid development of artificial intelligence (AI) and breakthroughs in Internet of Things (IoT) technologies have driven the innovation of advanced autonomous driving systems (ADSs). Image classification deep learning (DL) algorithms immensely contribute to the decision-making process in ADSs, showcasing their capabilities in handling complex real-world driving scenarios, surpassing human driving intelligence. However, these algorithms are vulnerable to adversarial attacks, which aim to fool them in real-time decision-making and compromise the reliability of the autonomous driving functions. This systematic review offers a comprehensive overview of the most recent literature on adversarial attacks and countermeasures on image classification DL models in ADSs. The review highlights the current challenges in applying successful countermeasures to mitigating these vulnerabilities. We also introduce taxonomies for categorizing adversarial attacks and countermeasures and provide recommendations and guidelines to help researchers design and evaluate countermeasures. We suggest interesting future research directions to improve the robustness of image classification DL models against adversarial attacks in autonomous driving scenarios.

Links and sources

• Official / arXiv page
• PDF from original source

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