research-article

An Image Privacy Protection Algorithm Based on Adversarial Perturbation Generative Networks

Authors:

Zhigao ZhengAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 17, Issue 2

Article No.: 43, Pages 1 - 14

https://doi.org/10.1145/3381088

Published: 21 April 2021 Publication History

Abstract

Today, users of social platforms upload a large number of photos. These photos contain personal private information, including user identity information, which is easily gleaned by intelligent detection algorithms. To thwart this, in this work, we propose an intelligent algorithm to prevent deep neural network (DNN) detectors from detecting private information, especially human faces, while minimizing the impact on the visual quality of the image. More specifically, we design an image privacy protection algorithm by training and generating a corresponding adversarial sample for each image to defend DNN detectors. In addition, we propose an improved model based on the previous model by training an adversarial perturbation generative network to generate perturbation instead of training for each image. We evaluate and compare our proposed algorithm with other methods on wider face dataset and others by three indicators: Mean average precision, Averaged distortion, and Time spent. The results show that our method significantly interferes with DNN detectors while causing weak impact to the visual quality of images, and our improved model does speed up the generation of adversarial perturbations.

References

[1]

Alessandro Acquisti and Christina M. Fong. 2020. An experiment in hiring discrimination via online social networks. Management Science 66, 3 (2020), 1005--1024.

Digital Library

[2]

Shumeet Baluja and Ian Fischer. 2017. Adversarial transformation networks: Learning to generate adversarial examples. arXiv preprint arXiv:1703.09387 (2017).

[3]

Joseph Bonneau, Jonathan Anderson, and Luke Church. 2009. Privacy suites: Shared privacy for social networks. In Proceedings of the Symposium on Usable Privacy and Security (SOUPS’09), Vol. 9. 1--2.

Digital Library

[4]

Avishek Joey Bose and Parham Aarabi. 2018. Adversarial attacks on face detectors using neural net-based constrained optimization. In Proceedings of the IEEE 20th International Workshop on Multimedia Signal Processing (MMSP’18). IEEE, 1--6.

[5]

Xueying Dong. 2018. A method of image privacy protection based on blockchain technology. In Proceedings of the International Conference on Cloud Computing, Big Data and Blockchain (ICCBB’18). IEEE, 1--4.

[6]

Mark Everingham, S. M. Ali Eslami, Luc Van Gool, Christopher K. I. Williams, John Winn, and Andrew Zisserman. 2015. The pascal visual object classes challenge: A retrospective. Int. J. Comput. Vision 111, 1 (2015), 98--136.

Digital Library

[7]

Lujun Fang and Kristen LeFevre. 2010. Privacy wizards for social networking sites. In Proceedings of the 19th International Conference on World Wide Web. ACM, 351--360.

Digital Library

[8]

Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy. 2015. Explaining and harnessing adversarial examples. Stat 1050 (2015), 20.

[9]

Yuwen He, Chunhong Zhang, Xinning Zhu, and Yang Ji. 2019. Generative adversarial network-based image privacy protection algorithm. In Proceedings of the 10th International Conference on Graphics and Image Processing (ICGIP’18), Vol. 11069. International Society for Optics and Photonics, 1106927.

[10]

Sandy Huang, Nicolas Papernot, Ian Goodfellow, Yan Duan, and Pieter Abbeel. 2017. Adversarial attacks on neural network policies. arXiv preprint arXiv:1702.02284 (2017).

[11]

Peter Klemperer, Yuan Liang, Michelle Mazurek, Manya Sleeper, Blase Ur, Lujo Bauer, Lorrie Faith Cranor, Nitin Gupta, and Michael Reiter. 2012. Tag, you can see it!: Using tags for access control in photo sharing. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 377--386.

Digital Library

[12]

Jernej Kos, Ian Fischer, and Dawn Song. 2018. Adversarial examples for generative models. In Proceedings of the IEEE Security and Privacy Workshops (SPW’18). IEEE, 36--42.

[13]

Richard McPherson, Reza Shokri, and Vitaly Shmatikov. 2016. Defeating image obfuscation with deep learning. arXiv preprint arXiv:1609.00408 (2016).

[14]

Seong Joon Oh, Mario Fritz, and Bernt Schiele. 2017. Adversarial image perturbation for privacy protection a game theory perspective. In Proceedings of the IEEE International Conference on Computer Vision (ICCV’17). IEEE, 1491--1500.

[15]

Ramprasad Ravichandran, Michael Benisch, Patrick Gage Kelley, and Norman M. Sadeh. 2009. Capturing social networking privacy preferences. In Proceedings of the International Symposium on Privacy Enhancing Technologies Symposium. Springer, 1--18.

[16]

Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. 2015. Faster R-CNN: Towards real-time object detection with region proposal networks. In Advances in Neural Information Processing Systems. MIT Press, 91--99.

Digital Library

[17]

Eleftherios Spyromitros-Xioufis, Symeon Papadopoulos, Adrian Popescu, and Yiannis Kompatsiaris. 2016. Personalized privacy-aware image classification. In Proceedings of the ACM on International Conference on Multimedia Retrieval. ACM, 71--78.

Digital Library

[18]

Anna Squicciarini, Dan Lin, Sushama Karumanchi, and Nicole DeSisto. 2012. Automatic social group organization and privacy management. In Proceedings of the 8th International Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom’12). IEEE, 89--96.

Digital Library

[19]

Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna Estrach, Dumitru Erhan, Ian Goodfellow, and Robert Fergus. 2014. Intriguing properties of neural networks. In 2nd International Conference on Learning Representations (ICLR'14).

[20]

Pedro Tabacof, Julia Tavares, and Eduardo Valle. 2016. Adversarial images for variational autoencoders. arXiv preprint arXiv:1612.00155 (2016).

[21]

Ashwini Tonge and Cornelia Caragea. 2015. Privacy prediction of images shared on social media sites using deep features. arXiv preprint arXiv:1510.08583 (2015).

[22]

Ashwini Kishore Tonge and Cornelia Caragea. 2016. Image privacy prediction using deep features. In Proceedings of the 30th AAAI Conference on Artificial Intelligence.

[23]

Michael J. Wilber, Vitaly Shmatikov, and Serge Belongie. 2016. Can we still avoid automatic face detection? In Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV’16). IEEE, 1--9.

[24]

Shuo Yang, Ping Luo, Chen-Change Loy, and Xiaoou Tang. 2016. Wider face: A face detection benchmark. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 5525--5533.

[25]

Jun Yu, Baopeng Zhang, Zhengzhong Kuang, Dan Lin, and Jianping Fan. 2016. iPrivacy: Image privacy protection by identifying sensitive objects via deep multi-task learning. IEEE Trans. Info. Forens. Secur. 12, 5 (2016), 1005--1016.

Digital Library

[26]

Sergej Zerr, Stefan Siersdorfer, Jonathon Hare, and Elena Demidova. 2012. Privacy-aware image classification and search. In Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 35--44.

Digital Library

[27]

Haoti Zhong, Anna Cinzia Squicciarini, David J. Miller, and Cornelia Caragea. 2017. A group-based personalized model for image privacy classification and labeling. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI’17), Vol. 17. 3952--3958.

[28]

Zhuotun Zhu, Lingxi Xie, and Alan L. Yuille. 2017. Object recognition with and without objects. In Proceedings of the 26th International Joint Conference on Artificial Intelligence. 3609--3615.

Cited By

Wang ZHou JWu GLiu SLuo PLi X(2024)Efficient Task-driven Video Data Privacy Protection for Smart Camera Surveillance SystemACM Transactions on Sensor Networks10.1145/362582520:4(1-21)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3625825
Si W(2024)Image data privacy protection technology based on reversible information hiding and robust secret sharingIntelligent Systems with Applications10.1016/j.iswa.2024.20039623(200396)Online publication date: Sep-2024
https://doi.org/10.1016/j.iswa.2024.200396
Li HWang JXiong NZhang YVasilakos ALuo X(2023)A Siamese Inverted Residuals Network Image Steganalysis Scheme based on Deep LearningACM Transactions on Multimedia Computing, Communications, and Applications10.1145/357916619:6(1-23)Online publication date: 12-Jul-2023
https://dl.acm.org/doi/10.1145/3579166
Show More Cited By

Index Terms

An Image Privacy Protection Algorithm Based on Adversarial Perturbation Generative Networks

Recommendations

Protecting image privacy through adversarial perturbation
Abstract
In current digital era, users of various social media upload photos which usually contain tremendous amount of private information on daily basis. Though the private information contained within photos can assist enterprises to provide users with ...
Geolocated Data Generation and Protection Using Generative Adversarial Networks
Modeling Decisions for Artificial Intelligence
Abstract
Data mining techniques allow us to discover patterns in large datasets. Nonetheless, data may contain sensitive information. This is especially true when data is georeferenced. Thus, an adversary could learn about individual whereabouts, points of ...
An Autoencoder-Based Image Anonymization Scheme for Privacy Enhanced Deep Learning
Data and Applications Security and Privacy XXXVII
Abstract
The development of deep learning (DL) technology is dependent on the availability of large-scale image datasets to train deep neural networks (DNNs) for image classification. However, many raw image datasets contain sensitive identity feature ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 17, Issue 2

May 2021

410 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3461621

Editor:
Alberto Del Bimbo
University of Firenze, Italy

Issue’s Table of Contents

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 April 2021

Online AM: 07 May 2020

Accepted: 01 January 2020

Revised: 01 December 2019

Received: 01 September 2019

Published in TOMM Volume 17, Issue 2

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

Project of National Engineering Laboratory for Internet Medical System and Application
the National Natural Science Foundation of China

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
242
Total Downloads

Downloads (Last 12 months)16
Downloads (Last 6 weeks)2

Reflects downloads up to 03 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Wang ZHou JWu GLiu SLuo PLi X(2024)Efficient Task-driven Video Data Privacy Protection for Smart Camera Surveillance SystemACM Transactions on Sensor Networks10.1145/362582520:4(1-21)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3625825
Si W(2024)Image data privacy protection technology based on reversible information hiding and robust secret sharingIntelligent Systems with Applications10.1016/j.iswa.2024.20039623(200396)Online publication date: Sep-2024
https://doi.org/10.1016/j.iswa.2024.200396
Li HWang JXiong NZhang YVasilakos ALuo X(2023)A Siamese Inverted Residuals Network Image Steganalysis Scheme based on Deep LearningACM Transactions on Multimedia Computing, Communications, and Applications10.1145/357916619:6(1-23)Online publication date: 12-Jul-2023
https://dl.acm.org/doi/10.1145/3579166
Ou LHe YLiao SQin ZHong YZhang DJia X(2023)FaceIDP: Face Identification Differential Privacy via Dictionary Learning Neural NetworksIEEE Access10.1109/ACCESS.2023.326026011(31829-31841)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3260260
Hupont ITolan SGunes HGómez E(2022)The landscape of facial processing applications in the context of the European AI Act and the development of trustworthy systemsScientific Reports10.1038/s41598-022-14981-612:1Online publication date: 23-Jun-2022
https://doi.org/10.1038/s41598-022-14981-6

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Figures

Tables

Media

View Issue’s Table of Contents