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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2507.21387 |
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| _version_ | 1866913964362825728 |
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| author | Xie, Hongyi |
| author_facet | Xie, Hongyi |
| contents | Surface electromyography (EMG) enables non-invasive human-computer interaction in rehabilitation, prosthetics, and virtual reality. While deep learning models achieve over 97% classification accuracy, their vulnerability to adversarial attacks remains largely unexplored in the physical domain. We present ERa Attack, the first radio frequency (RF) adversarial method targeting EMG devices through intentional electromagnetic interference (IEMI). Using low-power software-defined radio transmitters, attackers inject optimized RF perturbations to mislead downstream models. Our approach bridges digital and physical domains: we generate adversarial perturbations using Projected Gradient Descent, extract 50-150 Hz components via inverse STFT, and employ synchronization-free strategies (constant spectrum noise or narrowband modulation). Perturbations, constrained to 1-10% of signal amplitude, are amplitude-modulated onto 433 MHz carriers. Experiments on the Myo Dataset (7 gestures, 350 samples) demonstrate significant impact: at 1 meter and 0 dBm transmission power, classification accuracy drops from 97.8% to 58.3%, with 41.7% misclassification rate and 25.6% targeted attack success rate. Attack effectiveness decreases exponentially with distance, recovering to 85% accuracy at 3 meters. Increasing power to 10 dBm reduces accuracy by an additional 15% at 1 meter. This work pioneers RF-based adversarial attacks on EMG recognition systems, revealing critical vulnerabilities in safety-critical applications. We quantify attack effectiveness across different perturbation modes and distances, and propose defenses including hardware shielding, spectrum monitoring, and adversarial training. Our findings inform the design of robust EMG systems against electromagnetic threats. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_21387 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Radio Adversarial Attacks on EMG-based Gesture Recognition Networks Xie, Hongyi Cryptography and Security Surface electromyography (EMG) enables non-invasive human-computer interaction in rehabilitation, prosthetics, and virtual reality. While deep learning models achieve over 97% classification accuracy, their vulnerability to adversarial attacks remains largely unexplored in the physical domain. We present ERa Attack, the first radio frequency (RF) adversarial method targeting EMG devices through intentional electromagnetic interference (IEMI). Using low-power software-defined radio transmitters, attackers inject optimized RF perturbations to mislead downstream models. Our approach bridges digital and physical domains: we generate adversarial perturbations using Projected Gradient Descent, extract 50-150 Hz components via inverse STFT, and employ synchronization-free strategies (constant spectrum noise or narrowband modulation). Perturbations, constrained to 1-10% of signal amplitude, are amplitude-modulated onto 433 MHz carriers. Experiments on the Myo Dataset (7 gestures, 350 samples) demonstrate significant impact: at 1 meter and 0 dBm transmission power, classification accuracy drops from 97.8% to 58.3%, with 41.7% misclassification rate and 25.6% targeted attack success rate. Attack effectiveness decreases exponentially with distance, recovering to 85% accuracy at 3 meters. Increasing power to 10 dBm reduces accuracy by an additional 15% at 1 meter. This work pioneers RF-based adversarial attacks on EMG recognition systems, revealing critical vulnerabilities in safety-critical applications. We quantify attack effectiveness across different perturbation modes and distances, and propose defenses including hardware shielding, spectrum monitoring, and adversarial training. Our findings inform the design of robust EMG systems against electromagnetic threats. |
| title | Radio Adversarial Attacks on EMG-based Gesture Recognition Networks |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2507.21387 |