Frequency Matters: On the Impact of Carrier Frequency on Privacy in Radio Fingerprinting

Radio Frequency Fingerprinting (RFF) relies on unique inherent imperfections in radios’ hardware to authenticate devices based on Radio Frequency emissions. In this work, we consider that fingerprints collected for multi-channel transmitters on certain frequencies get partially leaked to an adversary willing to track them, without information about the frequency used for training. In this scenario, we evaluate the performance of various state-of-the-art Convolutional Neural Networks for image-based RFF when the testing and training frequencies do not match. We demonstrate that RFF performances degrade significantly when training and testing frequencies differ, down to a random guess when they are sufficiently apart.