RF Domain Backdoor Attack on Signal Classification via Stealthy Trigger
Zijie Tang, Tianming Zhao, Tianfang Zhang, Huy Phan, Yan Wang, Cong Shi, Bo Yuan, Yingying Chen
Abstract
Deep learning (DL) has recently become a key technology 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 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. Then, we design a training-based backdoor trigger generation approach with different optimization procedures for two backdoor attack scenarios (i.e., poison-label and clean-label). Extensive experiments on two RF signal classification datasets show that the attack success rate 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). The low NMSE (less than 0.091) indicates the stealthiness of the attack. Additionally, we demonstrate that our attack can bypass existing defense strategies, such as Neural Cleanse and STRIP.