标题： 无线电对抗性攻击在基于肌电图的手势识别网络上 显示英文标题

标题： Radio Adversarial Attacks on EMG-based Gesture Recognition Networks

摘要： 表面肌电图（EMG）在康复、假肢和虚拟现实领域实现了非侵入性的人机交互。 尽管深度学习模型的分类准确率超过97%，但它们在物理领域对对抗攻击的脆弱性仍大多未被研究。 我们提出了ERa Attack，这是第一个通过有意电磁干扰（IEMI）针对EMG设备的射频（RF）对抗方法。 使用低功率软件定义无线电发射器，攻击者注入优化的RF扰动以误导下游模型。 我们的方法连接了数字和物理领域：我们使用投影梯度下降生成对抗扰动，通过逆短时傅里叶变换提取50-150 Hz成分，并采用无同步策略（恒定频谱噪声或窄带调制）。 扰动被限制在信号幅度的1-10%范围内，并被振幅调制到433 MHz载波上。 在Myo数据集（7种手势，350个样本）上的实验表明有显著影响：在1米距离和0 dBm传输功率下，分类准确率从97.8%降至58.3%，误分类率为41.7%，目标攻击成功率为25.6%。 攻击效果随距离呈指数级下降，在3米处恢复到85%的准确率。 将功率增加到10 dBm在1米处使准确率进一步降低15%。 这项工作开创了基于射频的EMG识别系统的对抗攻击，揭示了在安全关键应用中的关键漏洞。 我们量化了不同扰动模式和距离下的攻击效果，并提出了包括硬件屏蔽、频谱监控和对抗训练在内的防御措施。 我们的研究结果为设计抵御电磁威胁的鲁棒EMG系统提供了依据。 显示英文摘要

摘要： 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.