Minimum Power Adversarial Attacks in Communication Signal Modulation Classification with Deep Learning

Abstract

Integrating cognitive radio (CR) technique with wireless networks is an effective way to solve the increasingly crowded spectrum. Automatic modulation classification (AMC) plays an important role in CR. AMC significantly improves the intelligence of CR system by classifying the modulation type and signal parameters of received communication signals. AMC can provide more information for decision making of the CR system. In addition, AMC can help the CR system dynamically adjust the modulation type and coding rate of the communication signal to adapt to different channel qualities, and the AMC technique help eliminate the cost of broadcast modulation type and coding rate. Deep learning (DL) has recently emerged as one most popular method in AMC of communication signals. Despite their success, DL models have recently been shown vulnerable to adversarial attacks in pattern recognition and computer vision. Namely, they can be easily deceived if a small and carefully designed perturbation called an adversarial attack is imposed on the input, typically an image in pattern recognition. Owing to the very different nature of communication signals, it is interesting yet crucially important to study if adversarial perturbation could also fool AMC. In this paper, we make a first attempt to investigate how we can design a special adversarial attack on AMC. we start from the assumption of a linear binary classifier which is further extended to multi-way classifier. We consider the minimum power consumption that is different from existing adversarial perturbation but more reasonable in the context of AMC. We then develop a novel adversarial perturbation generation method that leads to high attack success to communication signals. Experimental results on real data show that the method is able to successfully spoof the 11-class modulation classification at a model with a minimum cost of about − 21 dB in automatic modulation classification task. The visualization results demonstrate that the adversarial perturbation manifests in the time domain as imperceptible undulations of the signal, and in the frequency domain as small noise outside the signal band.