标题： 基于无线信号的AI人体活动分类中的微多普勒攻击 显示英文标题

标题： The micro-Doppler Attack Against AI-based Human Activity Classification from Wireless Signals

摘要： 基于人工智能算法使用被动收集的无线信号的人类活动分类（HAC）系统的一个子集。 本文提出了针对来自无线正交频分复用（OFDM）信号的人类活动分类的微多普勒攻击。 该攻击通过在传输的OFDM波形中插入人工变化，在其反射到人体目标时改变其微多普勒特征。 我们研究了我们方案的两种变体，这些变体在不同的时间尺度上操作波形，导致接收器频谱图发生变化。 使用深度卷积神经网络（CNN）的HAC准确率可以降低到10%以下。 显示英文摘要

摘要： A subset of Human Activity Classification (HAC) systems are based on AI algorithms that use passively collected wireless signals. This paper presents the micro-Doppler attack targeting HAC from wireless orthogonal frequency division multiplexing (OFDM) signals. The attack is executed by inserting artificial variations in a transmitted OFDM waveform to alter its micro-Doppler signature when it reflects off a human target. We investigate two variants of our scheme that manipulate the waveform at different time scales resulting in altered receiver spectrograms. HAC accuracy with a deep convolutional neural network (CNN) can be reduced to less than 10%.