Stealthy Backdoor Attack on RF Signal Classification

Tianming Zhao, Zijie Tang, Tianfang Zhang, Huy Phan, Yan Wang, Cong Shi, Bo Yuan, Yingying Chen

Abstract: Recently, deep learning (DL) has become one of the key technologies supporting radio frequency (RF) signal classification applications. Given the heavy DL training requirement, adopting outsourced training is a practical option for RF application developers. However, the outsourcing process exposes a security vulnerability that enables a backdoor attack. While backdoor attacks have been explored in the computer vision domain, it is rarely explored in the RF domain. In this work, we present a stealthy backdoor attack that targets DL-based RF signal classification. To realize such an attack, we extensively explore the characteristics of the RF data in different applications, which include RF modulation classification and RF fingerprint-based device identification. Particularly, we design a training-based backdoor trigger generation approach with an optimization procedure that not only accommodates dynamic application inputs but also is stealthy to RF receivers. Extensive experiments on two RF signal classification datasets show that the average attack success rate of our backdoor attack is over 99.2%, while its classification accuracy for the clean data remains high (i.e., less than a 0.6% drop compared to the clean model). Additionally, we demonstrate that our attack can bypass existing defense strategies, such as Neural Cleanse and STRIP.