Research on adversarial sample attack defense method of automatic modulation recognition system based on deep learning

Automatic modulation recognition of radio signals is a crucial research direction in non-cooperative communication and satellite communication domains. In recent years, deep learning has achieved remarkable results in the field of automatic modulation recognition. However, adversarial machine learning techniques have introduced threats of adversarial attacks to deep learning-based automatic modulation recognition systems. To enhance the adversarial security of automatic modulation recognition systems, this paper proposes a cross-representation domain ensemble adversarial training defense method. Experiments conducted on the public dataset RML2016.10a demonstrate that by integrating robust models generated through adversarial training in two representation domains, the proposed approach significantly improves the defense capability of automatic modulation recognition models against adversarial example attacks.