Universal adversarial attack method for communication modulation identification using principal component analysis

Deep learning is easily attacked by adversarial examples. Taking communication modulation recognition as an example, adding adversarial perturbations to the transmitted signal can effectively prevent non-cooperative users from utilizing the deep learning method to recognize the modulation of the signal. Thus, adversarial perturbations can help enhance communication security. To address the problem that the existing adversarial attack techniques are difficult to meet the adaptive and real-time requirements, the universal adversarial perturbation applicable to the whole dataset was obtained by the principal component analysis of the adversarial perturbation generated by a small part of the data extracted from the dataset. The computation of the universal adversarial perturbation can be carried out under offline conditions and then added to the signal to be transmitted in real time, which can satisfy the real-time requirements of communication and realize the purpose of reducing the accuracy of non-cooperative party modulation recognition. Experimental results show that the proposed method has better deception performance relative to the baseline method.