Robust filtering for multi-object tracking against stealthy measurement-oriented adversarial attacks

Measurement-oriented adversarial attacks, particularly ghost attacks, pose a significant challenge to the robustness of multi-object tracking (MOT) systems due to their covert nature. These attacks not only deceive the system and degrade tracking accuracy but also impose additional computational overhead proportional to the number of ghost targets. Under high-density attacks, the computational capacity of the sensing node can become fully saturated, potentially leading to a denialof- service (DoS) scenario. To ensure the integrity and accessibility of multi-sensor, multi-object tracking systems, this work introduces the Average Likelihood for Attack Resilient Multi-object (ALARM) filtering method. This approach enhances efficiency and robustness by effectively rejecting false measurements during the Bayesian update. The proposed method mitigates the impact of ghost measurements at an early stage and demonstrates significantly improved computational performance compared to standard filters under high-density attacks. This makes ALARM a practical and efficient solution, particularly for large-scale networks susceptible to attacks.