# Banshee: Target Switch Attacks on Gimbal-Stabilized Visual Tracking Systems via Acoustic Injection

arXiv: https://arxiv.org/abs/2607.09930 · HTML: https://mlsanity.com/paper/2607.09930/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 78 references that are in the map.
- First posted on arXiv 10 Jul 2026.

## Builds on (most cited first, 30 of 30)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks](https://mlsanity.com/paper/1812.11703/) (2018)
- [Learning Spatio-Temporal Transformer for Visual Tracking](https://mlsanity.com/paper/2103.17154/) (2021)
- [Learning Discriminative Model Prediction for Tracking](https://mlsanity.com/paper/1904.07220/) (2019)
- [ATOM: Accurate Tracking by Overlap Maximization](https://mlsanity.com/paper/1811.07628/) (2018)
- [Distractor-aware Siamese Networks for Visual Object Tracking](https://mlsanity.com/paper/1808.06048/) (2018)
- [YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Video](https://mlsanity.com/paper/1702.00824/) (2017)
- [TCTrack: Temporal Contexts for Aerial Tracking](https://mlsanity.com/paper/2203.01885/) (2022)
- [AutoTrack: Towards High-Performance Visual Tracking for UAV with Automatic Spatio-Temporal Regularization](https://mlsanity.com/paper/2003.12949/) (2020)
- [GIAOTracker: A comprehensive framework for MCMOT with global information and optimizing strategies in VisDrone 2021](https://mlsanity.com/paper/2202.11983/) (2022)
- [UCMCTrack: Multi-Object Tracking with Uniform Camera Motion Compensation](https://mlsanity.com/paper/2312.08952/) (2023)
- [Dynamic Coarse-to-Fine Learning for Oriented Tiny Object Detection](https://mlsanity.com/paper/2304.08876/) (2023)
- [Attacking Optical Flow](https://mlsanity.com/paper/1910.10053/) (2019)
- [Physical Adversarial Textures that Fool Visual Object Tracking](https://mlsanity.com/paper/1904.11042/) (2019)
- [Follow Anything: Open-set detection, tracking, and following in real-time](https://mlsanity.com/paper/2308.05737/) (2023)
- [TPatch: A Triggered Physical Adversarial Patch](https://mlsanity.com/paper/2401.00148/) (2023)
- [A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical Flow](https://mlsanity.com/paper/2203.13214/) (2022)
- [Siamese Transformer Pyramid Networks for Real-Time UAV Tracking](https://mlsanity.com/paper/2110.08822/) (2021)
- [Siamese Object Tracking for Unmanned Aerial Vehicle: A Review and Comprehensive Analysis](https://mlsanity.com/paper/2205.04281/) (2022)
- [Injected and Delivered: Fabricating Implicit Control over Actuation Systems by Spoofing Inertial Sensors](https://mlsanity.com/paper/1806.07558/) (2018)
- [ControlLoc: Physical-World Hijacking Attack on Visual Perception in Autonomous Driving](https://mlsanity.com/paper/2406.05810/) (2024)
- [Fast-Tracker: A Robust Aerial System for Tracking Agile Target in Cluttered Environments](https://mlsanity.com/paper/2011.03968/) (2020)
- [Elastic Tracker: A Spatio-temporal Trajectory Planner Flexible Aerial Tracking](https://mlsanity.com/paper/2109.07111/) (2021)
- [Adaptive Tracking and Perching for Quadrotor in Dynamic Scenarios](https://mlsanity.com/paper/2312.11866/) (2023)
- [Physical ID-Transfer Attacks against Multi-Object Tracking via Adversarial Trajectory](https://mlsanity.com/paper/2512.01934/) (2025)
- [VIMU: Effective Physics-based Realtime Detection and Recovery against Stealthy Attacks on UAVs](https://mlsanity.com/paper/2504.20569/) (2025)
- [MARS: Defending Unmanned Aerial Vehicles From Attacks on Inertial Sensors with Model-based Anomaly Detection and Recovery](https://mlsanity.com/paper/2505.00924/) (2025)
- [FlyTrap: Physical Distance-Pulling Attack Towards Camera-based Autonomous Target Tracking Systems](https://mlsanity.com/paper/2509.20362/) (2025)

## Abstract (arXiv)

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.