Recessive Social Networking:Preventing Privacy Leakage against Reverse Image Search

Zhang, Jiajie and Zhang, Bingsheng and Lin, Jiancheng (2019) Recessive Social Networking:Preventing Privacy Leakage against Reverse Image Search. In: 2019 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW). IEEE, pp. 211-219. ISBN 9781728130279

[img]
Text (ressive social)
ressive_social.pdf - Accepted Version
Available under License Creative Commons Attribution-NonCommercial.

Download (11MB)

Abstract

This work investigates the image privacy problem in the context of social networking under the threat of reverse image search. We introduce a new concept called recessive social networking. Unlike conventional privacy-preserving social networking, in our setting, the aim is to deceive machine learning algorithms that used in reverse image search, while still enabling unaffected ubiquitous social networking among humans. We, for the first time, ultilize adversarial example technique as a defensive mechanism to protect image privacy against content-based image search algorithms in the context of social networking. Finally, rigorous evaluations are conducted to demonstrate the effectiveness, transferability, and robustness of the proposed countermeasure.

Item Type:
Contribution in Book/Report/Proceedings
Additional Information:
©2019 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
ID Code:
136350
Deposited By:
Deposited On:
27 Aug 2019 12:45
Refereed?:
Yes
Published?:
Published
Last Modified:
19 Sep 2020 07:16