Deep neural networks (DNNs) greatly enable the task of automatic modulation classification (AMC) by virtue of their powerful feature extraction capability. However, extensive research has shown that DNNs are highly vulnerable to adversarial attacks, which can lead them to confidently output incorrect results with high confidence scores. Existing adversarial attack methods often focus solely on temporal characteristics of signals while neglecting frequency domain information, resulting in adversarial examples with poor transferability and inadequate performance in the closed-box scenario. An adversarial attack method based on frequency domain feature enhanced and integral gradient (FEIG) for AMC task is proposed in this paper. The approach utilizes techniques such as translation interpolation and Inverse Fast Fourier Transform to enhance the frequency domain information of original examples, thereby constructing enhanced baseline examples. Subsequently, these generated enhanced baseline examples are used as new inputs for gradient integration to obtain adversarial examples. Compared to traditional methods, the generated adversarial examples exhibit stronger transferability. Furthermore, in order to improve the defense performance of the model, an enhanced hybrid adversarial training (EH-AT) framework is proposed in this paper. The original clean example and the adversarial example generated by the proposed attack method are trained with joint loss constraints, which greatly enhances the robustness of the model. Experimental results demonstrate the effectiveness of the FEIG attack method and the EH-AT framework.

Due to the openness of wireless channels, wireless communication is vulnerable to be eavesdropped, which results in confidential information leakage. Physical Layer security (PLS) technology provides a new way to solve this hidden danger of Internet of Things system. However, traditional PLS methods are often restricted by limited communication resources and unknown instantaneous channel state information of eavesdroppers, which makes it challenging to strike a balance between security and reliability in the communication system. Therefore, an adversarial defense embedded waveform design (ADEWD) method for physical layer reliable communication (PLRC) is proposed in this paper. Firstly, we use generative adversarial networks to generate amplitude controllable adversarial perturbation, and then superimpose it with original communication signal to form an adversarial signal. At the same time, we also design a demodulation network based on the modulation type of legitimate users to constrain the amplitude of the generated perturbations, to reduce the bit error rate (BER) loss after demodulation of the adversarial signal. With this waveform design, the adversarial signal not only enables reliable communication between legitimate users, but also utilizes embedded defense traps to prevent eavesdroppers from recognizing legitimate users. The experimental results demonstrate that our ADEWD method for PLRC has stronger defense capability and lower BER in both white-box and black-box scenarios, which reflects the defense robustness and communication reliability of the proposed waveform design method.