  Adversarial evasion attacks have been very successful in causing poor
performance in a wide variety of machine learning applications. One such
application is radio frequency spectrum sensing. While evasion attacks have
proven particularly successful in this area, they have done so at the detriment
of the signal's intended purpose. More specifically, for real-world
applications of interest, the resulting perturbed signal that is transmitted to
evade an eavesdropper must not deviate far from the original signal, less the
intended information is destroyed. Recent work by the authors and others has
demonstrated an attack framework that allows for intelligent balancing between
these conflicting goals of evasion and communication. However, while these
methodologies consider creating adversarial signals that minimize
communications degradation, they have been shown to do so at the expense of the
spectral shape of the signal. This opens the adversarial signal up to defenses
at the eavesdropper such as filtering, which could render the attack
ineffective. To remedy this, this work introduces a new spectral deception loss
metric that can be implemented during the training process to force the
spectral shape to be more in-line with the original signal. As an initial proof
of concept, a variety of methods are presented that provide a starting point
for this proposed loss. Through performance analysis, it is shown that these
techniques are effective in controlling the shape of the adversarial signal.
