  Specific Emitter Identification (SEI) detects, characterizes, and identifies
emitters by exploiting distinct, inherent, and unintentional features in their
transmitted signals. Since its introduction, a significant amount of work has
been conducted; however, most assume the emitters are passive and that their
identifying signal features are immutable and challenging to mimic. Suggesting
the emitters are reluctant and incapable of developing and implementing
effective SEI countermeasures; however, Deep Learning (DL) has been shown
capable of learning emitter-specific features directly from their raw in-phase
and quadrature signal samples, and Software-Defined Radios (SDRs) can
manipulate them. Based on these capabilities, it is fair to question the ease
at which an emitter can effectively mimic the SEI features of another or
manipulate its own to hinder or defeat SEI. This work considers SEI mimicry
using three signal features mimicking countermeasures; off-the-self DL; two
SDRs of different sizes, weights, power, and cost (SWaP-C); handcrafted and
DL-based SEI processes, and a coffee shop deployment. Our results show
off-the-shelf DL algorithms, and SDR enables SEI mimicry; however, adversary
success is hindered by: the use of decoy emitter preambles, the use of a
denoising autoencoder, and SDR SWaP-C constraints.
