Natural and effective obfuscation by head inpainting

• Main Authors: SUN, Qianru, MA, Liqian, OH, Seong Joon, VAN GOOL, Luc, SCHIELE, Bernt, FRITZ, Mario
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2018
• Subjects: Person image generation; identity obfuscation; privacy protection; generative adversarial networks; Artificial Intelligence and Robotics; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/4457; https://ink.library.smu.edu.sg/context/sis_research/article/5460/viewcontent/Sun_Natural_and_Effective_CVPR_2018_paper__1_.pdf
• Institution: Singapore Management University

As more and more personal photos are shared online, being able to obfuscate identities in such photos is becoming a necessity for privacy protection. People have largely resorted to blacking out or blurring head regions, but they result in poor user experience while being surprisingly ineffective against state of the art person recognizers. In this work, we propose a novel head inpainting obfuscation technique. Generating a realistic head inpainting in social media photos is challenging because subjects appear in diverse activities and head orientations. We thus split the task into two sub-tasks: (1) facial landmark generation from image context (e.g. body pose) for seamless hypothesis of sensible head pose, and (2) facial landmark conditioned head inpainting. We verify that our inpainting method generates realistic person images, while achieving superior obfuscation performance against automatic person recognizers.