Robust Adversarial Attacks against DNN-Based Wireless Communication Systems

Abstract

There is significant enthusiasm for the employment of Deep Neural Networks (DNNs) for important tasks in major wireless communication systems: channel estimation and decoding in orthogonal frequency division multiplexing (OFDM) systems, end-to-end autoencoder system design, radio signal classification, and signal authentication. Unfortunately, DNNs can be susceptible to adversarial examples, potentially making such wireless systems fragile and vulnerable to attack. In this work, by designing robust adversarial examples that meet key criteria, we perform a comprehensive study of the threats facing DNN-based wireless systems. We model the problem of adversarial wireless perturbations as an optimization problem that incorporates domain constraints specific to different wireless systems. This allows us to generate wireless adversarial perturbations that can be applied to wireless signals on-the-fly (i.e., with no need to know the target signals a priori), are undetectable from natural wireless noise, and are robust against removal. We show that even in the presence of significant defense mechanisms deployed by the communicating parties, our attack performs significantly better compared to existing attacks against DNN-based wireless systems. In particular, the results demonstrate that even when employing well-considered defenses, DNN-based wireless communication systems are vulnerable to adversarial attacks and call into question the employment of DNNs for a number of tasks in robust wireless communication.