  In this paper we propose a method for defending against an eavesdropper that
uses a Deep Neural Network (DNN) for learning the modulation of wireless
communication signals. Our method is based on manipulating the emitted waveform
with the aid of a continuous time frequency-modulated (FM) obfuscating signal
that is mixed with the modulated data. The resulting waveform allows a
legitimate receiver (LRx) to demodulate the data but it increases the test
error of a pre-trained or adversarially-trained DNN classifier at the
eavesdropper. The scheme works for analog modulation and digital single carrier
and multi carrier orthogonal frequency division multiplexing (OFDM) waveforms,
while it can implemented in frame-based wireless protocols. The results
indicate that careful selection of the parameters of the obfuscating waveform
can drop classification performance at the eavesdropper to less than 10% in
AWGN and fading channels with no performance loss at the LRx.
