Dafx: Digital Audio Effects
Privacy protecting data collection in media spaces
Proceedings of the 12th annual ACM international conference on Multimedia
Preserving Privacy by De-Identifying Face Images
IEEE Transactions on Knowledge and Data Engineering
Automatically protecting privacy in consumer generated videos using intended human object detector
Proceedings of the international conference on Multimedia
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Group video-conferencing systems are routinely used in major corporations, hospitals and universities for meetings, tele-medicine and distance learning among participants from very distant locations. As the use of video-conferencing becomes widely prevalent, the privacy concern's raised by this technology becomes an important issue to be addressed. In this paper we propose a real-time privacy preserving video conferencing system which protects the visual and audio privacy of selected individuals. In our proposed system we differentiate between the general participants and private participants (PP) whose privacy needs to be protected. We further divide the private participants into two different categories and provide a varying level of privacy protection based on the requirements. Specifically, among private participants, we have Active Private Participants (APP) who interactively participate in the meeting and Passive Private Participants (PPP) who play a passive observatory role. The video and audio privacy of the APP are protected by obfuscating their visual information by simple black boxing and real-time pitch modification process respectively. For the PPP, we completely protect their privacy by continuously detecting their presence and erasing them with a real-time adaptive background replacement process