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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Impact Evaluation of Falsified Data Attacks on Connected Vehicle...

Shihong Huang (University of Michigan, Ann Arbor), Yiheng Feng (Purdue University), Wai Wong (University of Michigan, Ann Arbor), Qi Alfred Chen (UC Irvine), Z. Morley Mao and Henry X. Liu (University of Michigan, Ann Arbor) Best Paper Award Runner-up ($200 cash prize)!

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On Building the Data-Oblivious Virtual Environment

Tushar Jois (Johns Hopkins University), Hyun Bin Lee, Christopher Fletcher, Carl A. Gunter (University of Illinois at Urbana-Champaign)

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