AdvScan detects adversarial examples in black-box TinyML on ARM Cortex-M devices via one-sample t-test on runtime power signatures against a benign baseline, reporting 99.984% detection with 40 false negatives and zero false positives over 318400 inputs.
Benchmarking TinyML Systems: Challenges and Direction
5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 5verdicts
UNVERDICTED 5representative citing papers
A HW-NAS framework executable on resource-limited embedded devices generates optimized CNNs for low-end MCUs and reports state-of-the-art human-recognition accuracy on the Visual Wake Word dataset.
A Z-score anomaly detector trained and inferred fully on an STM32 microcontroller using power side-channel RMS data achieves perfect precision and recall on a 14-day fridge dataset with low memory and latency.
Network-adaptive encoding reduces end-to-end latency in cloud-based visual preprocessing for neuroprostheses during congestion while preserving global scene structure at the cost of sharper boundary degradation.
Reports INT8 autoencoder TinyML models for on-device arrhythmia detection from ECG, achieving 84% recall and 9 ms latency on ESP32 after filtering ambiguous cases.
citing papers explorer
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Fully Autonomous Z-Score-Based TinyML Anomaly Detection on Resource-Constrained MCUs Using Power Side-Channel Data
A Z-score anomaly detector trained and inferred fully on an STM32 microcontroller using power side-channel RMS data achieves perfect precision and recall on a 14-day fridge dataset with low memory and latency.
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ArrythML: An Autoencoder-Based TinyML Approach for On-Device Arrhythmia Detection on Resource-Constrained Embedded Systems
Reports INT8 autoencoder TinyML models for on-device arrhythmia detection from ECG, achieving 84% recall and 9 ms latency on ESP32 after filtering ambiguous cases.