  Radio frequency fingerprint identification (RFFI) is an emerging technique
for the lightweight authentication of wireless Internet of things (IoT)
devices. RFFI exploits unique hardware impairments as device identifiers, and
deep learning is widely deployed as the feature extractor and classifier for
RFFI. However, deep learning is vulnerable to adversarial attacks, where
adversarial examples are generated by adding perturbation to clean data for
causing the classifier to make wrong predictions. Deep learning-based RFFI has
been shown to be vulnerable to such attacks, however, there is currently no
exploration of effective adversarial attacks against a diversity of RFFI
classifiers. In this paper, we report on investigations into white-box attacks
(non-targeted and targeted) using two approaches, namely the fast gradient sign
method (FGSM) and projected gradient descent (PGD). A LoRa testbed was built
and real datasets were collected. These adversarial examples have been
experimentally demonstrated to be effective against convolutional neural
networks (CNNs), long short-term memory (LSTM) networks, and gated recurrent
units (GRU).
