Adversarial Sample Generation Method for Modulated Signals Based on Edge-Linear Combination

In complex electromagnetic environments, wireless communication system reliability can be compromised by various types of jamming. To address the issue of jammers using deep neural network models to identify communication signal modulation method and apply targeted interference, this paper proposes a method for generating adversarial samples of modulation signals based on the Mixup linear combination approach. The method generates edge-linear combination samples with small perturbations by linearly combining the original signal samples near the decision edges, and then inputs them into the neural network model for identification test, determines the best perturbation signals for each type of signals according to the identification results, and then generates the adversarial samples by selecting the best perturbation signals for each type of modulation during the attack. Simulation results show that, compared to traditional gradient-based adversarial sample generation algorithms, the proposed method performs better under white-box attacks. Under black-box attacks, the proposed method achieves higher attack success rates and lower attack signal-to-noise ratios compared to random noise adversarial samples with the same disturbance coefficient.