Discrete Unintentional Adversarial Perturbation Detection for DQN Traffic Signal Control

Summary

Researchers study backdoor poisoning of deep Q-network (DQN) traffic signal controllers and their inference-time trigger activation. They propose DUIAPD, a detector that adds discretized random perturbations to each input and measures action-wise Q-value variability against a calibrated threshold, without training a surrogate network or updating DQN parameters. On a single SUMO intersection with four actions across 18 trigger configurations, it reports true positive rates of 92.14–96.38% and false positive rates of 1.51–6.40%, with generalization to other sensors, intersections, real-time use and closed-loop benefits still to be validated.

Topics

• Adversarial machine learning

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