  We consider a wireless communication system that consists of a background
emitter, a transmitter, and an adversary. The transmitter is equipped with a
deep neural network (DNN) classifier for detecting the ongoing transmissions
from the background emitter and transmits a signal if the spectrum is idle.
Concurrently, the adversary trains its own DNN classifier as the surrogate
model by observing the spectrum to detect the ongoing transmissions of the
background emitter and generate adversarial attacks to fool the transmitter
into misclassifying the channel as idle. This surrogate model may differ from
the transmitter's classifier significantly because the adversary and the
transmitter experience different channels from the background emitter and
therefore their classifiers are trained with different distributions of inputs.
This system model may represent a setting where the background emitter is a
primary user, the transmitter is a secondary user, and the adversary is trying
to fool the secondary user to transmit even though the channel is occupied by
the primary user. We consider different topologies to investigate how different
surrogate models that are trained by the adversary (depending on the
differences in channel effects experienced by the adversary) affect the
performance of the adversarial attack. The simulation results show that the
surrogate models that are trained with different distributions of
channel-induced inputs severely limit the attack performance and indicate that
the transferability of adversarial attacks is neither readily available nor
straightforward to achieve since surrogate models for wireless applications may
significantly differ from the target model depending on channel effects.
