Label Hijacking in Track Consensus-Based Distributed Multi-Target Tracking

TL;DR

This paper uncovers vulnerabilities in distributed multi-target tracking systems, demonstrating how adversaries can inject spoofed data to corrupt target identities, and emphasizes the importance of enhancing robustness in consensus-based tracking networks.

Contribution

It introduces the concept of label hijacking attacks in TC-DMTT, formalizes attack stealthiness, and provides an optimization strategy to craft such attacks, highlighting robustness issues.

Findings

Label hijacking can successfully impersonate targets across sensors.

The attack significantly reduces tracking accuracy and label consistency.

The study underscores the need for more robust DMTT frameworks.

Abstract

Distributed multi-target tracking (DMTT) in limited field-of-view (FoV) sensor networks commonly suffers from label inconsistency, whereby different nodes disagree on the identity of the same target. Recent track-consensus DMTT (TC-DMTT) strategies mitigate this issue by enforcing kinematic and label agreement through metric-based track matching. Nevertheless, their behavior under adversarial conditions remains largely unexplored. In this paper, we reveal identity-level vulnerabilities in TC-DMTT and introduce the concept of label hijacking: an attack in which an adversary injects spoofed tracks to corrupt target identities across the network. Drawing on an analogy to classical pull-off deception in radar, we formalize a notion of attack stealthiness and derive an optimization-based strategy for crafting such attacks. A three-sensor network case study demonstrates the impact of the proposed attack on label consistency and tracking accuracy, showing successful target impersonation. Overall, this work highlights the need to rethink robustness at the consensus layer in DMTT frameworks.