Mohd Sabra (University of Texas at San Antonio), Anindya Maiti (University of Oklahoma), Murtuza Jadliwala (University of Texas at San Antonio)

Due to recent world events, video calls have become the new norm for both personal and professional remote communication. However, if a participant in a video call is not careful, he/she can reveal his/her private information to others in the call. In this paper, we design and evaluate an attack framework to infer one type of such private information from the video stream of a call -- keystrokes, i.e., text typed during the call. We evaluate our video-based keystroke inference framework using different experimental settings, such as different webcams, video resolutions, keyboards, clothing, and backgrounds. Our high keystroke inference accuracies under commonly occurring experimental settings highlight the need for awareness and countermeasures against such attacks. Consequently, we also propose and evaluate effective mitigation techniques that can automatically protect users when they type during a video call.

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Hai Huang (Tsinghua University), Jiaming Mu (Tsinghua University), Neil Zhenqiang Gong (Duke University), Qi Li (Tsinghua University), Bin Liu (West Virginia University), Mingwei Xu (Tsinghua University)

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Yu-Chuan Liang, Hsu-Chun Hsiao (National Taiwan University)

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Trust the Crowd: Wireless Witnessing to Detect Attacks on...

Kai Jansen (Ruhr University Bochum), Liang Niu (New York University), Nian Xue (New York University), Ivan Martinovic (University of Oxford), Christina Pöpper (New York University Abu Dhabi)

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HERA: Hotpatching of Embedded Real-time Applications

Christian Niesler (University of Duisburg-Essen), Sebastian Surminski (University of Duisburg-Essen), Lucas Davi (University of Duisburg-Essen)

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