arXiv subjects

Robi Paul

Publications and source records attributed to Robi Paul.

2 recordsLinked to original sources

arXiv2026

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 while inducing minimal latency. AdvScan 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, it 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 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.

cs.CR↗

arXiv2025

Michscan: Black-Box Neural Network Integrity Checking at Runtime Through Power Analysis

As neural networks are increasingly used for critical decision-making tasks, the threat of integrity attacks, where an adversary maliciously alters a model, has become a significant security and safety concern. These concerns are compounded by the use of licensed models, where end-users purchase third-party models with only black-box access to protect model intellectual property (IP). In such scenarios, conventional approaches to verify model integrity require knowledge of model parameters or cooperative model owners. To address this challenge, we propose Michscan, a methodology leveraging power analysis to verify the integrity of black-box TinyML neural networks designed for resource-constrained devices. Michscan is based on the observation that modifications to model parameters impact the instantaneous power consumption of the device. We leverage this observation to develop a runtime model integrity-checking methodology that employs correlational power analysis using a golden template or signature to mathematically quantify the likelihood of model integrity violations at runtime through the Mann-Whitney U-Test. Michscan operates in a black-box environment and does not require a cooperative or trustworthy model owner. We evaluated Michscan using an STM32F303RC microcontroller with an ARM Cortex-M4 running four TinyML models in the presence of three model integrity violations. Michscan successfully detected all integrity violations at runtime using power data from five inferences. All detected violations had a negligible probability P < 10^(-5) of being produced from an unmodified model (i.e., false positive).

cs.CR↗