  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.
