Robust Adversarial Attacks on Deep Learning-Based RF Fingerprint Identification
Boyang Liu, Haoran Zhang, Yiyao Wan, Fuhui Zhou, Qihui Wu, Derrick Wing Kwan Ng
Abstract
Deep learning (DL)-based radio frequency fingerprint identification (RFFI), despite its state-of-the-art capability in improving the security performance of communication networks, is still vulnerable to carefully crafted and imperceptible adversarial attack. However, conventional radio frequency (RF) adversarial attacks ignore the impact of the practical fading channels between the attacker and the sensor. In this letter, a generation adversarial perturbations (GAP) problem is formulated in a DL-based RFFI network. Both the case of ideally perfect channel state information (CSI) and the practical imperfect CSI are considered. The closed-form expression of the adversarial example based on the perfect CSI is provided. In order to address the intractable non-convex robust optimization problem under the imperfect CSI, a spoofing attack algorithm is proposed by exploiting the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathcal{ S}}$ </tex-math></inline-formula> -procedure. Simulation results demonstrate that our proposed scheme achieves a superior spoofing ratio compared with other benchmark schemes.