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

Summary

AdvScan is a black-box method for detecting adversarial examples on TinyML models at runtime by analyzing power consumption. It builds a baseline of power signatures from benign inputs and applies a one-sample t-test to flag inputs whose signatures deviate significantly. Evaluated on three MLPerf Tiny models across two STM32 microcontrollers and 318,400 test inputs, it detected 99.984% of adversarial examples, with 40 false negatives and no false positives.

Topics

• Adversarial machine learning

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