OTDA: a Unsupervised Optimal Transport framework with Discriminant Analysis for Keystroke Inference

Keystroke Inference has been a hot topic since it poses a severe threat to our privacy from typing. Existing learning-based Keystroke Inference suffers the domain adaptation problem because the training data (from attacker) and the test data (from victim) are generally collected in different environments. Recently, Optimal Transport (OT) is applied to address this problem, but suffers the “ground metric” limitation. In this work, we propose a novel method, OTDA, by incorporating Discriminant Analysis into OT through an iterative learning process to address the ground metric limitation. By embedding OTDA into a vibration-based Keystroke Inference platform, we conduct extensive studies about domain adaptation with different factors, such as people, keyboard position, etc.. Our experiment results show that OTDA can achieve significant performance improvement on classification accuracy, i.e., outperforming baseline by 15% to 30%, state-of-the-art OT and other domain adaptation methods by 10% to 20%.