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

TinyML models deployed on edge devices are increasingly adopted in safety/security-critical applications, making them a prime target for adversarial example (AE) attacks where inputs are modified to cause misclassifications. However, existing AE detection methods either require white-box model access, which is often unavailable in licensed black-box deployments, or rely on input pre-processing stages that add non-trivial latency and resource overhead, often exceeding what mission-critical applications can afford on their inference path. To address these challenges, we propose AdvScan, a runtime power analysis-based methodology for AE detection that operates in a black-box scenario and while inducing minimal latency. AdvS-can is based on the observation that AEs produce anomalous neuron activations, which, in turn, generate distinctive power-consumption signatures. The algorithm initially constructs a baseline distribution of power signatures from known benign inputs, then, at runtime, applies a one-sample t-test to determine whether a test input’s power signature significantly deviates from this baseline, thereby detecting AEs. We evaluated AdvScan using three adversarial example (AE) generation algorithms (Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and Carlini–Wagner (C&W)) on three MLPerf Tiny benchmark models implemented on two target devices: the STM32F303RC (ARM Cortex-M4) and STM32L562RE (ARM Cortex-M33) microcontrollers. Across 318,400 total test inputs, AdvScan detects 99.984% of AEs with only 40 false negatives and zero false positives. These results demonstrate the viability of power-based AE detection for secure, accuracy-critical TinyML deployments in black-box environments.