  Artificial intelligence (AI) technology has provided a potential solution for
automatic modulation recognition (AMC). Unfortunately, AI-based AMC models are
vulnerable to adversarial examples, which seriously threatens the efficient,
secure and trusted application of AI in AMC. This issue has attracted the
attention of researchers. Various studies on adversarial attacks and defenses
evolve in a spiral. However, the existing adversarial attack methods are all
designed in the time domain. They introduce more high-frequency components in
the frequency domain, due to abrupt updates in the time domain. For this issue,
from the perspective of frequency domain, we propose a spectrum focused
frequency adversarial attacks (SFFAA) for AMC model, and further draw on the
idea of meta-learning, propose a Meta-SFFAA algorithm to improve the
transferability in the black-box attacks. Extensive experiments, qualitative
and quantitative metrics demonstrate that the proposed algorithm can
concentrate the adversarial energy on the spectrum where the signal is located,
significantly improve the adversarial attack performance while maintaining the
concealment in the frequency domain.
