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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Securing CAN Traffic on J1939 Networks

Jeremy Daily, David Nnaji, and Ben Ettlinger (Colorado State University)

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Your Phone is My Proxy: Detecting and Understanding Mobile...

Xianghang Mi (University at Buffalo), Siyuan Tang (Indiana University Bloomington), Zhengyi Li (Indiana University Bloomington), Xiaojing Liao (Indiana University Bloomington), Feng Qian (University of Minnesota Twin Cities), XiaoFeng Wang (Indiana University Bloomington)

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