Using adversarial noises to protect privacy in deep learning era
Version 2 2024-06-03, 11:49Version 2 2024-06-03, 11:49
Version 1 2019-04-05, 23:04Version 1 2019-04-05, 23:04
conference contribution
posted on 2024-06-03, 11:49authored byB Liu, M Ding, T Zhu, Yong XiangYong Xiang, W Zhou
The unprecedented accuracy of deep learning methods has earned themselves as the foundation of new AI-based services on the Internet. At the same time, it presents obvious privacy issues. The deep learning aided privacy attack can dig out sensitive personal information not only from the text but also from unstructured data such as images and videos. In this paper, we proposed a framework to protect image privacy against the deep learning tools. We also propose two new metrics to measure the image privacy. Moreover, we propose two different image privacy protection schemes based on the two metrics, utilizing the adversarial example idea. The performance of our schemes is validated by simulation on a large-scale dataset. Our study shows that we can protect the image privacy by adding a small amount of noise, while the added noise has a humanly imperceptible impact on the image quality.
History
Pagination
1-6
Location
Abu Dhabi, United Arab Emirates
Start date
2018-12-09
End date
2018-12-13
ISBN-13
9781538647271
Language
eng
Publication classification
E1 Full written paper - refereed
Copyright notice
2018, IEEE
Editor/Contributor(s)
[Unknown]
Title of proceedings
GLOBECOM 2018 : Proceedings of the 2018 IEEE Global Communications Conference
Event
IEEE Communications Society. Conference (2018 : Abu Dhabi, United Arab Emirates)