女妖：通过声学注入对万向节稳定视觉跟踪系统进行目标切换攻击
Banshee: Target Switch Attacks on Gimbal-Stabilized Visual Tracking Systems via Acoustic Injection
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中文总结 AI 辅助
研究针对万向节稳定视觉跟踪系统，提出女妖攻击方法，利用声学漏洞诱导目标切换，通过生成特定声学波形使相机视图漂移，打破帧间目标关联，在模拟和实际测试中取得高成功率，揭示了声学与视觉跨域漏洞。
中文摘要 AI 辅助
万向节稳定视觉跟踪对无人机等现代自主系统至关重要。先前工作表明声学信号会干扰万向节内部，但对无人机跟踪等实际应用的影响研究不足。现有演示忽视了实际攻击的挑战。为此提出女妖攻击，利用万向节 - 相机系统声学漏洞诱导无人机视觉跟踪系统目标切换。精心生成声学波形引发对抗性万向节振荡，导致相机视图漂移打破帧间目标关联，使机载跟踪器大概率切换目标。在模拟中成功率达93.6%，实际测试成功率达95.5%。揭示了声学与视觉间实际跨域漏洞，凸显万向节系统和应用稳健设计的必要性。
英文摘要
Gimbal-stabilized visual tracking is critical for modern autonomous systems such as Unmanned Aerial Vehicles (UAVs). While prior work shows acoustic signals can disturb gimbal internals, the impact of such attacks on real-world applications like UAV tracking and following remains underexplored. Existing demonstrations largely overlook practical challenges for real-world attacks, such as object-motion uncertainty and runtime latency. To bridge this gap, we present Banshee, the first physically realizable attack that induces target switching in UAV visual tracking systems by exploiting acoustic vulnerabilities in gimbal-camera systems. Banshee generates carefully crafted acoustic waveforms that induce optimized adversarial gimbal oscillations, causing directionally biased camera-view drifts that break inter-frame target associations. Consequently, the onboard tracker is driven to switch from the original target to an attacker-selected object with high probability, with occasional target loss. Banshee achieves a 93.6% success rate in simulation across two commercial gimbal systems and five trackers. Real-world benchtop and in-flight black-box attacks against a commercial drone across varied scenarios show an overall 95.5% attack success rate. Our results reveal a practical cross-domain vulnerability between acoustics and vision, highlighting the need for robust designs of gimbal systems and applications. Our code is available at: https://github.com/U1ltra/Banshee.
