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Security and Communication Networks
Volume 2017, Article ID 1897438, 15 pages
Research Article

Protecting Privacy in Shared Photos via Adversarial Examples Based Stealth

University of Science and Technology of China, Hefei, China

Correspondence should be addressed to Weiming Zhang; nc.ude.ctsu@mwgnahz

Received 19 July 2017; Revised 1 October 2017; Accepted 10 October 2017; Published 14 November 2017

Academic Editor: Lianyong Qi

Copyright © 2017 Yujia Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Online image sharing in social platforms can lead to undesired privacy disclosure. For example, some enterprises may detect these large volumes of uploaded images to do users’ in-depth preference analysis for commercial purposes. And their technology might be today’s most powerful learning model, deep neural network (DNN). To just elude these automatic DNN detectors without affecting visual quality of human eyes, we design and implement a novel Stealth algorithm, which makes the automatic detector blind to the existence of objects in an image, by crafting a kind of adversarial examples. It is just like all objects disappear after wearing an “invisible cloak” from the view of the detector. Then we evaluate the effectiveness of Stealth algorithm through our newly defined measurement, named privacy insurance. The results indicate that our scheme has considerable success rate to guarantee privacy compared with other methods, such as mosaic, blur, and noise. Better still, Stealth algorithm has the smallest impact on image visual quality. Meanwhile, we set a user adjustable parameter called cloak thickness for regulating the perturbation intensity. Furthermore, we find that the processed images have transferability property; that is, the adversarial images generated for one particular DNN will influence the others as well.