Adversarial Attack on Communication Signal Modulation Recognition

Convolutional network models (CNN) are very vulnerable to adversarial samples, which poses a serious challenge to the security of CNN models. Based on the task of CNN's modulation and identification of communication signals, we propose a white-box attack algorithm, the shortest distance attack method (SD-Alg), which can generate extremely small disturbances and greatly reduce the classification performance of the model. Experiments show that our algorithm excels in attack success rate, running time and adversarial perturbation size among the same type of algorithms.