  Modulation Classification (MC) refers to the problem of classifying the
modulation class of a wireless signal. In the wireless communications pipeline,
MC is the first operation performed on the received signal and is critical for
reliable decoding. This paper considers the problem of secure modulation
classification, where a transmitter (Alice) wants to maximize MC accuracy at a
legitimate receiver (Bob) while minimizing MC accuracy at an eavesdropper
(Eve).
  The contribution of this work is to design novel adversarial learning
techniques for secure MC. In particular, we present adversarial filtering based
algorithms for secure MC, in which Alice uses a carefully designed adversarial
filter to mask the transmitted signal, that can maximize MC accuracy at Bob
while minimizing MC accuracy at Eve. We present two filtering based algorithms,
namely gradient ascent filter (GAF), and a fast gradient filter method (FGFM),
with varying levels of complexity.
  Our proposed adversarial filtering based approaches significantly outperform
additive adversarial perturbations (used in the traditional ML community and
other prior works on secure MC) and also have several other desirable
properties. In particular, GAF and FGFM algorithms are a) computational
efficient (allow fast decoding at Bob), b) power-efficient (do not require
excessive transmit power at Alice); and c) SNR efficient (i.e., perform well
even at low SNR values at Bob).
