Transferable Sparse Adversarial Attack on Modulation Recognition With Generative Networks

Although Deep neural networks (DNN) can achieve higher performance in automatic modulation recognition, they are known to vulnerable to adversarial perturbations, which are strategically added to inputs can fool the DNN model. In this letter, we propose a novel sparse attack scheme based on adversarial generative networks, which enables more covert attacks while preserving communication quality. This new scheme incorporates adversarial training into the discriminator, which not only improves attack performance but also enhances the stability of the training process for adversarial generative networks. Experimental results demonstrate that the proposed scheme outperforms other sparse attack approaches in terms of generation time and transferability.