AdvScan: Black-Box Adversarial Example Detection at Runtime Through Power Analysis

AdvScan is a method for detecting adversarial examples (inputs slightly modified to trick AI models into making wrong predictions) on tiny machine learning models running on edge devices (small hardware like microcontrollers) without needing access to the model's internal details. The approach monitors power consumption patterns during the model's operation, since adversarial examples create unusual power signatures that differ from normal inputs, and uses statistical analysis to flag suspicious inputs in real-time with minimal performance overhead.