Research on the transferability of adversarial example attacks in automatic modulation recognition systems based on deep learning

Aiming at the problem of automatic modulation recognition based on deep learning in non-cooperative communication, the migration problem of adversarial sample attacks in both the model and the representation domain is studied. The experimental results show that both the model transfer and the representation domain transfer of the modulation recognition data after the adversarial sample attack will cause the attack effect to decline. The model transfer has a better effect and is more stable than the representation domain transfer. This research provides a new perspective for understanding the robustness and generalization laws of adversarial attacks in non-cooperative communication systems, and lays a theoretical foundation for designing more robust defense mechanisms for deep learning-driven signal processing tasks.