Towards a General Video-based Keystroke Inference Attack


Zhuolin Yang, Yuxin Chen, and Zain Sarwar, University of Chicago; Hadleigh Schwartz, Columbia University; Ben Y. Zhao and Haitao Zheng, University of Chicago


A large collection of research literature has identified the privacy risks of keystroke inference attacks that use statistical models to extract content typed onto a keyboard. Yet existing attacks cannot operate in realistic settings, and rely on strong assumptions of labeled training data, knowledge of keyboard layout, carefully placed sensors or data from other side-channels. This paper describes experiences developing and evaluating a general, video-based keystroke inference attack that operates in common public settings using a single commodity camera phone, with no pretraining, no keyboard knowledge, no local sensors, and no side-channels. We show that using a self-supervised approach, noisy finger tracking data from a video can be processed, labeled and filtered to train DNN keystroke inference models that operate accurately on the same video. Using IRB approved user studies, we validate attack efficacy across a variety of environments, keyboards, and content, and users with different typing behaviors and abilities. Our project website is located at:

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@inproceedings {285471,
author = {Zhuolin Yang and Yuxin Chen and Zain Sarwar and Hadleigh Schwartz and Ben Y. Zhao and Haitao Zheng},
title = {Towards a General Video-based Keystroke Inference Attack},
booktitle = {32nd USENIX Security Symposium (USENIX Security 23)},
year = {2023},
isbn = {978-1-939133-37-3},
address = {Anaheim, CA},
pages = {141--158},
url = {},
publisher = {USENIX Association},
month = aug

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