Deep learning (DL) architectures have been successfully used in many
applications including wireless systems. However, they have been shown to be
susceptible to adversarial attacks. We analyze DL-based models for a regression
problem in the context of downlink power allocation in massive
multiple-input-multiple-output systems and propose universal adversarial
perturbation (UAP)-crafting methods as white-box and black-box attacks. We
benchmark the UAP performance of white-box and black-box attacks for the
considered application and show that the adversarial success rate can achieve
up to 60% and 40%, respectively. The proposed UAP-based attacks make a more
practical and realistic approach as compared to classical white-box attacks.

本文研究了基于 DL 的模型在无线系统下行功率分配问题中的回归应用，并提出了通用对抗扰动（UAP）制造方法作为白盒和黑盒攻击。通过对 UAP 的性能测试，展示了其白盒和黑盒攻击成功率分别可以达到 60％和 40％。提出的 UAP 攻击方法与传统的白盒攻击相比更具实用性和现实性。