Learning to Anonymize Faces for
Privacy Preserving Action Detection

Yong Jae Lee¹ ²

¹EgoVid Inc.

²UC Davis

European Conference on Computer Vision (ECCV), 2018.

Abstract

There is an increasing concern in computer vision devices invading the privacy of their users. We want the camera systems/robots to recognize important events and assist human daily life by understanding its videos, but we also want to ensure that they do not intrude people's privacy. In this paper, we propose a new principled approach for learning a video anonymizer. We use an adversarial training setting in which two competing systems fight: (1) a video anonymizer that modifies the original video to remove privacy-sensitive information (i.e., human face) while still trying to maximize spatial action detection performance, and (2) a discriminator that tries to extract privacy-sensitive information from such anonymized videos. The end goal is for the video anonymizer to perform a pixel-level modification of video frames to anonymize each person's face, while minimizing the effect on action detection performance. We experimentally confirm the benefit of our approach particularly compared to conventional hand-crafted video/face anonymization methods including masking, blurring, and noise adding.

Video

Qualitative Results

The same image before and after anonymization.
The picture on the left of each pair is the original image, and the one on the right is the modified image.
The first four rows are from the face dataset (CASIA); the bottom two are from the video dataset (DALY).

ECCV DEMO

We have done a real-time demo at ECCV'18 (Munich, Germany). Our code is able to run at real-time (~30 FPS) and capture up to 7 people in the scene on a laptop with one Nvidia Geforce 1080 GPU. This speed is achieved with no special optimization on our PyTorch code, which will also be shared. There is definitely a lot of space to further improve the visual quality and running speed. Some interesting pictures of ourselves are here:

teaserImage

Activity-Preserving Face Anonymization for Privacy Protection.
Zhongzheng Ren, Yong Jae Lee, Hyun Jong Yang, Michael S. Ryoo.
ECCV Demo Session, Munich, Germany, 2018

Materials

[Paper] [User Study] [Poster] [Bibtex] [Reddit]

Talk

Check out the beautiful slides of Yong Jae from here.

Code

Please contact Prof.Michael Ryoo (mryoo-at-egovid.com).

Related Work

Please also check this wonderful follow-up work

Password-conditioned Anonymization and Deanonymization with Face Identity Transformers.
Xiuye Gu, Weixin Luo, Michael S. Ryoo, Yong Jae Lee. arXiv:1911.11759

Acknowledgments

This research was conducted as a part of EgoVid Inc.'s research activity on privacy-preserving computer vision. This work was supported by the Technology development Program (S2557960) funded by the Ministry of SMEs and Startups (MSS, Korea). We thank all the subjects who participated in our user study. We also thank Chongruo Wu, Fanyi Xiao, Krishna Kumar Singh, and Maheen Rashid for their valuable discussions on this work.

Comments, questions to Jason Ren

Learning to Anonymize Faces for Privacy Preserving Action Detection