综述自动驾驶感知中对抗样本的攻防研究，揭示神经网络安全风险。
Adversarial Examples in Environment Perception for Automated Driving (Review)
- 梳理十年来对抗攻击与防御方法的发展脉络
- 指出对抗扰动可导致自动驾驶误判，威胁行车安全
- 适合关注AI可信性与自动驾驶安全的研究者
深度学习的复兴推动了自动驾驶技术的快速发展。然而，深度神经网络容易受到对抗样本的影响：这些扰动对人眼不可察觉，却可能导致神经网络产生错误预测，给自动驾驶等AI应用带来巨大安全隐患。本文系统回顾了过去十年对抗鲁棒性研究的进展，涵盖攻击与防御方法及其在自动驾驶中的应用。自动驾驶的发展也促进了可信AI的实现。文中列出了该领域具有里程碑意义的研究文献。
原文摘要 · Abstract (English)
The renaissance of deep learning has led to the massive development of automated driving. However, deep neural networks are vulnerable to adversarial examples. The perturbations of adversarial examples are imperceptible to human eyes but can lead to the false predictions of neural networks. It poses a huge risk to artificial intelligence (AI) applications for automated driving. This survey systematically reviews the development of adversarial robustness research over the past decade, including the attack and defense methods and their applications in automated driving. The growth of automated driving pushes forward the realization of trustworthy AI applications. This review lists significant references in the research history of adversarial examples.
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