arXiv 2007.162042020-07-31eess.SPcs.LGcs.NIstat.ML

基于深度学习的调制分类器的多个卷积码对抗攻击

Adversarial Attacks with Multiple Antennas Against Deep Learning-Based Modulation Classifiers

University of Maryland（马里兰大学）；Intelligent Automation, Inc.（智能自动化公司）；Virginia Tech（弗吉尼亚理工大学）

Brian Kim, Yalin E. Sagduyu, Tugba Erpek, Kemal Davaslioglu, Sennur Ulukus

摘要：

我们考虑一个无线通信系统，其中发射机向接收机发送具有不同调制类型的信号，而接收机使用其基于深度学习的分类器对接收信号的调制类型进行分类。同时，对手使用其多个天线发送对抗性扰动，以欺骗分类器对接收到的信号进行错误分类。从对抗机器学习的角度来看，我们展示了如何利用对手的多个天线来提高对抗（逃避）攻击的性能。在利用多天线对抗时，主要考虑两个问题，即天线间的功率分配和信道分集的利用。首先，我们表明，多个独立的对手，每个与一个天线不能提高攻击性能相比，一个单一的对手与多个天线使用相同的总功率。然后，我们考虑各种方式来分配功率在多个天线之间在一个单一的对手，如分配功率只有一个天线，并成正比或成反比的信道增益。通过利用信道分集，我们引入攻击，通过符号级具有最大信道增益的信道传输对抗性扰动。我们发现，这种攻击降低了分类器的准确性显着相比，其他攻击在不同的信道条件下的信道方差和信道相关性天线。此外，我们表明，攻击的成功显着提高天线的数量增加，在对手，可以更好地利用信道多样性，工艺对抗性攻击。

英文摘要：

We consider a wireless communication system, where a transmitter sends signals to a receiver with different modulation types while the receiver classifies the modulation types of the received signals using its deep learning-based classifier. Concurrently, an adversary transmits adversarial perturbations using its multiple antennas to fool the classifier into misclassifying the received signals. From the adversarial machine learning perspective, we show how to utilize multiple antennas at the adversary to improve the adversarial (evasion) attack performance. Two main points are considered while exploiting the multiple antennas at the adversary, namely the power allocation among antennas and the utilization of channel diversity. First, we show that multiple independent adversaries, each with a single antenna cannot improve the attack performance compared to a single adversary with multiple antennas using the same total power. Then, we consider various ways to allocate power among multiple antennas at a single adversary such as allocating power to only one antenna, and proportional or inversely proportional to the channel gain. By utilizing channel diversity, we introduce an attack to transmit the adversarial perturbation through the channel with the largest channel gain at the symbol level. We show that this attack reduces the classifier accuracy significantly compared to other attacks under different channel conditions in terms of channel variance and channel correlation across antennas. Also, we show that the attack success improves significantly as the number of antennas increases at the adversary that can better utilize channel diversity to craft adversarial attacks.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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