English(EN) Adversarial Attacks on Robot Localization Systems via Deep Feature Perturbation

新的攻击方法通过深度特征扰动针对机器人定位系统

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-02 04:00

研究人员开发了一种新的方法，利用深度特征扰动来攻击机器人定位系统。他们的框架采用轻量级乘积量化网络（LPQN），生成微妙但有效的对抗性查询，误导系统的检索过程。这可能导致机器人定位错误，从而在关键应用中导致导航错误或不安全交互。实验证实了该方法显著降低了性能，并突显了实际机器人环境中的漏洞。 AI

影响强调了自主导航系统中潜在的安全漏洞，需要针对复杂的对抗性攻击建立强大的防御措施。

排序理由这是一篇详细介绍机器人定位系统对抗性攻击新方法的学术论文。

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arXiv cs.CV TIER_1 English(EN) · Zhenyu Li, Tianyi Shang · 2026-06-02 04:00

通过深度特征扰动对机器人定位系统进行对抗性攻击

arXiv:2606.01892v1 Announce Type: new Abstract: Robot localization systems are critical for autonomous navigation and safety. Adversarial perturbations can mislead these systems, resulting in mislocalization, navigation errors, or unsafe interactions, especially in mission-critic…