标题： 基于GAN的对抗攻击生成器针对智能端到端基于自动编码器的通信系统 显示英文标题

标题： GAN-based Generator of Adversarial Attack on Intelligent End-to-End Autoencoder-based Communication System

摘要： 深度神经网络已被应用于无线通信系统以智能地适应动态变化的信道条件，但用户仍受到恶意攻击的威胁，这是由于无线信道的广播特性。 然而，大多数攻击模型需要目标的详细信息，这在实际系统中难以实现。 我们的目标是开发一种无需目标信息即可增强数据块误码率的攻击模型。 在我们的设计中，我们提出了一种基于新型生成对抗网络（GANs）的攻击架构，该架构利用了深度学习模型容易受到动态变化信道条件引起的扰动影响的特性。 在所提出的生成器中，攻击网络由卷积层、转置卷积层和全连接层组成。 然后我们提出了训练策略和训练算法的细节。 随后，我们提出了验证策略来评估生成器的性能。 进行了仿真，并且结果表明，我们提出的对抗攻击生成器在加性白高斯噪声（AWGN）信道、瑞利信道和高速铁路信道上的数据块误码率攻击性能优于基准方案。 显示英文摘要

摘要： Deep neural networks have been applied in wireless communications system to intelligently adapt to dynamically changing channel conditions, while the users are still under the threat of the malicious attacks due to the broadcasting property of wireless channels. However, most attack models require the knowledge of the target details, which is difficult to be implemented in real systems. Our objective is to develop an attack model with no requirement for the target information, while enhancing the block error rate. In our design, we propose a novel Generative Adversarial Networks(GANs) based attack architecture, which exploits the property of deep learning models being vulnerable to perturbations induced by dynamically changing channel conditions. In the proposed generator, the attack network is composed of convolution layer, convolution transpose layer and linear layer. Then we present the training strategy and the details of the training algorithm. Subsequently, we propose the validation strategy to evaluate the performance of the generator. Simulations are conducted and the results show that our proposed adversarial attack generator achieve better block error rate attack performance than that of benchmark schemes over Additive White Gaussian Noise (AWGN) channel, Rayleigh channel and High-Speed Railway channel.