Puzzling face verification algorithms for privacy protection

Binod Bhattarai, Alexis Mignon, Frédéric Jurie, Teddy Furon

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution


This paper presents a new approach for de-identifying face images, i.e. for preventing automatic matching with public face collections. The overall motivation is to offer tools for privacy protection on social networks. We address this question by drawing a parallel between face de-identification and oracle attacks in digital watermarking. In our case, the identity of the face is seen as the watermark to be removed. Inspired by oracle attacks, we forge de-identified faces by superimposing a collection of carefully designed noise patterns onto the original face. The modification of the image is controlled to minimize the probability of good recognition while minimizing the distortion. In addition, these de-identified images are - by construction - made robust to counter attacks such as blurring. We present an experimental validation in which we de-identify LFW faces and show that resulting images are still recognized by human beings while deceiving a state-of-the-art face recognition algorithm.
Original languageEnglish
Title of host publication2014 IEEE International Workshop on Information Forensics and Security (WIFS)
PublisherIEEE Explore
Number of pages6
ISBN (Electronic)978-1-4799-8882-2
Publication statusPublished - 2014

Bibliographical note

2014 IEEE International Workshop on Information Forensics and Security (WIFS), 03-05 December 2014, Atlanta, GA, USA


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