基于蒸馏与剪枝的深度学习无线分类器的对抗鲁棒性
Adversarial Robustness of Distilled and Pruned Deep Learning-based Wireless Classifiers
AI总结：
针对无线信号自动调制分类在边缘应用中易受对抗攻击的问题，提出结合知识蒸馏与网络剪枝的优化模型并辅以高效对抗训练，在提升干净样本准确率的同时显著增强了对抗鲁棒性。
摘要：
针对无线信号自动调制分类（AMC）开发的基于数据驱动的深度学习（DL）技术容易受到对抗攻击。这对基于DL的无线系统构成了严重的安全威胁，特别是对于AMC的边缘应用。在本研究中，我们解决了开发既优化又对对抗攻击具有鲁棒性的DL模型的联合问题。这使得基于DL的AMC能够在边缘设备上高效可靠地部署。我们首先提出了两种使用知识蒸馏和网络剪枝的优化模型，随后采用一种计算高效的对抗训练过程来提升鲁棒性。在五种白盒攻击上的实验结果表明，所提出的优化且经过对抗训练的模型比标准（未优化）模型具有更好的鲁棒性。这两种优化模型在干净（未受攻击）样本上也达到了更高的准确率，这对于边缘应用中基于DL解决方案的可靠性至关重要。
英文摘要：
Data-driven deep learning (DL) techniques developed for automatic modulation classification (AMC) of wireless signals are vulnerable to adversarial attacks. This poses a severe security threat to the DL-based wireless systems, specifically for edge applications of AMC. In this work, we address the joint problem of developing optimized DL models that are also robust against adversarial attacks. This enables efficient and reliable deployment of DL-based AMC on edge devices. We first propose two optimized models using knowledge distillation and network pruning, followed by a computationally efficient adversarial training process to improve the robustness. Experimental results on five white-box attacks show that the proposed optimized and adversarially trained models can achieve better robustness than the standard (unoptimized) model. The two optimized models also achieve higher accuracy on clean (unattacked) samples, which is essential for the reliability of DL-based solutions at edge applications.
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