Researchers From the University of Sydney and Adobe Propose P3M-Net: A Portrait Matting Model with Privacy Preserving

Deep learning’s effectiveness in many computer vision and multimedia applications is primarily dependent on vast amounts of training data. However, privacy concerns concerning individually identifiable information in datasets, such as face, gait, and voice, have lately gained traction for particular activities such as face recognition, human activity analysis, and portrait animation.

Unfortunately, addressing data privacy issues without compromising performance remains difficult and under-explored. Portrait matting, which involves predicting accurate foregrounds from portrait photographs, is particularly vulnerable to privacy concerns, as …

adobe computervision privacy researchers sydney university