TrackShield: An Efficient Runtime Defense Against Availability Attacks on High-Performance Real-Time Multi-Object Tracking
cs.CR
Submitted: 2026-10-13
Updated: 2026-10-13
Code: https://github.com/ershang2/SlowTrack
License: http://creativecommons.org/licenses/by/4.0/
The gist: High-performance camera-based perception pipelines must sustain per-frame real-time budgets on resource-constrained embedded GPU platforms.
Abstract
High-performance camera-based perception pipelines must sustain per-frame real-time budgets on resource-constrained embedded GPU platforms. Availability attacks such as SlowTrack inflate the persistent state of multi-object trackers: data-association workload accumulates across frames, so per-frame latency increases and drives the pipeline past its deadline. Detector- and NMS-layer defenses do not directly control this persistent tracker-side resource. We present TrackShield, a lightweight runtime resource-management layer that protects the tracker at the point where attack-induced workload accumulates. TrackShield combines low-overhead anomaly monitoring with staged birth capping, pruning of young or weakly matched tracks, and per-stage association-input admission control. Its hard admission guard provides a deterministic bound on admitted association workload, independently of the heuristic monitor. On a Jetson AGX Orin, TrackShield reduces attacked tracking-stage latency from 139.90 to 6.27,ms and whole-pipeline P99 latency from 190.9 to 48.8,ms, at a benign MOTA cost of 1.06 points. As an availability defense, TrackShield focuses on keeping the tracker responsive under attack instead of recovering the full tracking accuracy degraded by adversarial detections.
Sources
- YOLOX: Exceeding YOLO Series in 2021
- BoT-SORT: Robust Associations Multi-Pedestrian Tracking
- Deep Learning Latency Attacks and Defenses: A Cross-Domain Survey of Availability Threats
- Overload: Latency Attacks on Object Detection for Edge Devices
- TrackFlood: Relocating Latency Attacks from NMS-Free Detectors to Real-Time Trackers
- SlowPerception: Physical-World Latency Attack against Visual Perception in Autonomous Driving
- GateDrain: Availability Attacks and Admission-Side Defense for Confidence-Gated Edge-Cloud Inference
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