The use of Deep Learning (DL) in wireless communication systems is becoming very
popular. As an example to the use of DL, the end-to-end (E2E) communication system can …

Deep Learning (DL), in spite of its huge success in many new fields, is extremely vulnerable
to adversarial attacks. We demonstrate how an attacker applies physical white-box and …

We show that end-to-end learning of communication systems through deep neural network
autoencoders can be extremely vulnerable to physical adversarial attacks. Specifically, we …

We consider a wireless communication system that consists of a transmitter, a receiver, and
an adversary. The transmitter transmits signals with different modulation types, while the …

Deep learning-based semantic communications (DLSC) replace the physical blocks in
traditional communication systems as end-to-end neural networks. DLSC significantly boost …

Deep learning, as widely known, is vulnerable to adversarial samples. This paper focuses
on the adversarial attack on autoencoders. Safety of the autoencoders (AEs) is important …

Deep neural networks (DNNs) are increasingly being used in a variety of traditional
radiofrequency (RF) problems. Previous work has shown that while DNN classifiers are …

There is significant enthusiasm for the employment of Deep Neural Networks (DNNs) for
important tasks in major wireless communication systems: channel estimation and decoding …

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 …

Despite their well-documented learning capabilities in clean environments, deep
convolutional neural networks (CNNs) are extremely fragile in adversarial settings, where …