The advancements in Automatic Modulation Classification (AMC) have propelled the development of signal sensing and identification technologies in non-cooperative communication scenarios but also enable eavesdroppers to effectively intercept user signals in wireless communication environments. To protect user privacy in communication links, we have optimized the adversarial example generation model and introduced a novel framework for generating adversarial perturbations for transmitted signals. This framework implements dual-domain constraints in both the time and frequency domains, ensuring that the adversarial perturbation cannot be filtered out. Comparative experiments confirm the superiority of the proposed method and the concealment of the adversarial examples it generates.