AIR: Threats of Adversarial Attacks on Deep Learning-Based Information
  Recovery
Jinyin Chen Jie Ge Shilian Zheng Linhui Ye Haibin Zheng Weiguo Shen Keqiang Yue Xiaoniu Yang
A wireless communications system usually consists of a transmitter which
transmits the information and a receiver which recovers the original
information from the received distorted signal. Deep learning (DL) has been
used to improve the performance of the receiver in complicated channel
environments and state-of-the-art (SOTA) performance has been achieved.
However, its robustness has not been investigated. In order to evaluate the
robustness of DL-based information recovery models under adversarial
circumstances, we investigate adversarial attacks on the SOTA DL-based
information recovery model, i.e., DeepReceiver. We formulate the problem as an
optimization problem with power and peak-to-average power ratio (PAPR)
constraints. We design different adversarial attack methods according to the
adversary's knowledge of DeepReceiver's model and/or testing samples. Extensive
experiments show that the DeepReceiver is vulnerable to the designed attack
methods in all of the considered scenarios. Even in the scenario of both model
and test sample restricted, the adversary can attack the DeepReceiver and
increase its bit error rate (BER) above 10%. It can also be found that the
DeepReceiver is vulnerable to adversarial perturbations even with very low
power and limited PAPR. These results suggest that defense measures should be
taken to enhance the robustness of DeepReceiver.