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Khamis, Mohamed; Seitz, Tobias; Mertl, Leonhard; Nguyen, Alice; Schneller, Mario; Li, Zhe (2019): Passquerade: Improving Error Correction of Text Passwords on Mobile Devices by using Graphic Filters for Password Masking. In: Chi 2019: Proceedings of the 2019 Chi Conference on Human Factors in Computing Systems
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Entering text passwords on mobile devices is a significant challenge. Current systems either display passwords in plain text: making them visible to bystanders, or replace characters with asterisks shortly after they are typed: making editing them harder. This work presents a novel approach to mask text passwords by distorting them using graphical filters. Distorted passwords are difficult to observe by attackers because they cannot mentally reverse the distortions. Yet passwords remain readable by their owners because humans can recognize visually distorted versions of content they saw before. We present results of an online questionnaire and a user study where we compared Color-halftone, Crystallize, Blurring, and Mosaic filters to Plain text and Asterisks when 1) entering, 2) editing, and 3) shoulder surfing one-word passwords, random character passwords, and passphrases. Rigorous analysis shows that Color-halftone and Crystallize filters significantly improve editing speed, editing accuracy and observation resistance compared to current approaches.