ABSTRACT
Prior research literature shows that there has been considerable work done in the last decade in the area of image de-identification (DeID) for privacy protection. With the advances made in privacy enhancing image DeID techniques, there have been research studies on different DeID performance evaluation approaches for determining the effectiveness of these methods. Existing approaches for evaluating DeID methods can be classified into three separate categories - analysis of privacy versus utility, analysis of viewer experience-based user studies, and analysis of robustness against adversarial attacks. However, none of these categorized approaches have utilized person re-identification (ReID) for evaluating DeID. Additionally, there are no previous research studies that have analyzed the threat of ReID to DeID. In this paper, we present a unique experimental case study that demonstrates how ReID can be used successfully for evaluating the efficacy of DeID techniques, and how, in the process, we can assess the threat of ReID to DeID. We describe a novel approach, in which a selected ReID algorithm is pitted against multiple DeID techniques to test the robustness of these DeID methods, and to determine if ReID can pose a threat to DeID as an adversary. Through this approach, we compare the DeID performances based upon how effectively they can deter successful ReID in the privacy enhanced versions of the ReID image dataset. Our preliminary results show how we can potentially evaluate DeID and compare DeID performances by analyzing the extents to which they are able to successfully resist re-identification i.e., by studying the impact of DeID on the ReID performances.
- Latanya Sweeney. 2002. k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 10, 05 (2002), 557-570. DOI: http://dx.doi.org/10.1142/s0218488502001648Google ScholarDigital Library
- Apurva Bedagkar-Gala and Shishir K. Shah. 2014. A survey of approaches and trends in person re-identification. Image and Vision Computing 32, 4 (2014), 270–286. DOI: http://dx.doi.org/10.1016/j.imavis.2014.02.001Google ScholarDigital Library
- Rui Zhao, Wanli Ouyang, and Xiaogang Wang. 2014. Learning Mid-level Filters for Person Re-identification. IEEE Conference on Computer Vision and Pattern Recognition (2014). DOI: http://dx.doi.org/10.1109/cvpr.2014.26Google ScholarDigital Library
- Ankur Chattopadhyay and Donxay Rasovang. 2018. Future Directions in Privacy-Enhancing Video Surveillance. IEEE Future Directions - Technology Policy & Ethics Newsletter. March 2018 Issue. DOI: https://cmte.ieee.org/futuredirections/tech-policy-ethics/2018articles/future-directions-in-privacy-enhancing-video-surveillance/Google ScholarCross Ref
- Ankur Chattopadhyay and T.E. Boult. 2007. PrivacyCam: a Privacy Preserving Camera Using uCLinux on the Blackfin DSP. IEEE Conference on Computer Vision and Pattern Recognition (2007). DOI: http://dx.doi.org/10.1109/cvpr.2007.383413Google ScholarCross Ref
- Srikrishna Karanam, Mengran Gou, Ziyan Wu, Angels Rates-Borras, Octavia Camps, and Richard J. Radke. 2019. A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets. IEEE Transactions on Pattern Analysis and Machine Intelligence 41, 3, 523-536. DOI: http://dx.doi.org/10.1109/tpami.2018.2807450Google ScholarDigital Library
- Zhong Zhang and R.S. Blum. 1999. A categorization of multiscale-decomposition-based image fusion schemes with a performance study for a digital camera application. Proceedings of the IEEE 87, 8 (1999), 1315-1326. DOI: http://dx.doi.org/10.1109/5.775414Google ScholarCross Ref
- Paul Viola and Michael J. Jones. 2001. Robust real-time face detection. Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001. DOI: http://dx.doi.org/10.1109/iccv.2001.937709Google ScholarCross Ref
- Gary Bradski and Adrian Kaehler. 2008. Learning OpenCV: Computer Vision with the OpenCV Library. O'Reilly Media, Inc. DOI: https://www.oreilly.com/library/view/learning-opencv/9780596516130/Google Scholar
- Kyungnam Kim, Thanarat H. Chalidabhongse, David Harwood, and Larry Davis. 2005. Real-time foreground–background segmentation using codebook model. Real-Time Imaging 11, 3 (2005), 172–185. DOI: http://dx.doi.org/10.1016/j.rti.2004.12.004Google ScholarDigital Library
- Ingrid Daubechies. 1992. Ten Lectures on Wavelets. USA: Society for Industrial and Applied Mathematics. Philadelhia, PA, USA. DOI: http://dx.doi.org/10.1137/1.9781611970104Google Scholar
- Geoffrey M. Davis and Aria Nosratinia. 1999. Wavelet-Based Image Coding: An Overview. Applied and Computational Control, Signals, and Circuits. B. N. Datta, Ed., Birkhäuser Boston, pp. 369-434. DOI: http://dx.doi.org/10.1007/978-1-4612-0571-5_8Google Scholar
- Marc Antonini, Michel Barlaud, Pierre Mathieu and Ingrid Daubechies. 1992. Image coding using wavelet transform. IEEE Transactions on Image Processing, vol. 1, no. 2, pp. 205-220Google ScholarDigital Library
- Richard McPherson, Reza Shokri, and Vitaly Shmatikov. 2016. Defeating image obfuscation with deep learning. arXiv preprint arXiv:1609.00408. DOI: https://arxiv.org/abs/1609.00408Google Scholar
- Natacha Ruchaud and Jean-Luc Dugelay. 2017. ASePPI: Robust Privacy Protection Against De-Anonymization Attacks. IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). DOI: http://dx.doi.org/10.1109/cvprw.2017.177Google Scholar
- Zhenyu Wu, Zhangyang Wang, Zhaowen Wang, and Hailin Jin. 2018. Towards privacy-preserving visual recognition via adversarial training: A pilot study. Proceedings of the European Conference on Computer Vision (ECCV). Lecture Notes in Computer Science, 627-645. DOI: http://dx.doi.org/10.1007/978-3-030-01270-0_37Google ScholarDigital Library
- Francesco Pittaluga, Sanjeev Koppal, and Ayan Chakrabarti. 2019. Learning privacy preserving encodings through adversarial training. IEEE Winter Conference on Applications of Computer Vision (WACV). DOI: http://dx.doi.org/10.1109/wacv.2019.00089Google Scholar
- Kimia Tajik, Akshith Gunasekaran, Rhea Dutta, Brandon Ellis, Rakesh B. Bobba, Mike Rosulek, Charles V. Wright, and Wu-chi Feng. 2019. Balancing Image Privacy and Usability with Thumbnail-Preserving Encryption. Proceedings of Network and Distributed System Security Symposium - IACR Cryptol. ePrint Arch. p. 295. DOI: http://dx.doi.org/10.14722/ndss.2019.23432Google Scholar
- Tribhuvanesh Orekondy, Mario Fritz, and Bernt Schiele. 2018. Connecting pixels to privacy and utility: Automatic redaction of private information in images. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. DOI: http://dx.doi.org/10.1109/cvpr.2018.00883Google Scholar
- Seong J. Oh, Rodrigo Benenson, Mario Fritz, and Bernt Schiele. 2016. Faceless person recognition: Privacy implications in social media. European Conference on Computer Vision. Springer, ECCV - Lecture Notes in Computer Science, 19-35. DOI: http://dx.doi.org/10.1007/978-3-319-46487-9_2Google Scholar
- Seong J. Oh, Mario Fritz, and Bernt Schiele. 2017. Adversarial image perturbation for privacy protection a game theory perspective. IEEE International Conference on Computer Vision (ICCV). DOI: http://dx.doi.org/10.1109/iccv.2017.165Google Scholar
- Qianru Sun, Liqian Ma, Seong J. Oh, Luc Van Gool, Bernt Schiele, and Mario Fritz. 2018. Natural and effective obfuscation by head inpainting. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. DOI: http://dx.doi.org/10.1109/cvpr.2018.00530Google Scholar
- Qianru Sun, Ayush Tewari, Weipeng Xu, Mario Fritz, Christian Theobalt, and Bernt Schiele. 2018. A hybrid model for identity obfuscation by face replacement. Proceedings of the European Conference on Computer Vision (ECCV). Lecture Notes in Computer Science, 570-586. DOI: http://dx.doi.org/10.1007/978-3-030-01246-5_34Google ScholarDigital Library
- Yifang Li, Nishant Vishwamitra, Bart P. Knijnenburg, Hongxin Hu, and Kelly Caine. 2017. Effectiveness and users' experience of obfuscation as a privacy-enhancing technology for sharing photos. Proceedings of the ACM on Human-Computer Interaction 1. CSCW, pp. 1-24. DOI: http://dx.doi.org/10.1145/3134702Google ScholarDigital Library
- Pavel Korshunov, Andrea Melle, Jean-Luc Dugelay, and Touradj Ebrahimi. 2013. Framework for objective evaluation of privacy filters. Applications of Digital Image Processing XXXVI. International Society for Optics and Photonics. DOI: http://dx.doi.org/10.1117/12.2027040Google Scholar
- Zhongzheng Ren, Yong J. Lee, and Michael S. Ryoo. 2018. Learning to anonymize faces for privacy preserving action detection. Proceedings of the European Conference on Computer Vision (ECCV) - Lecture Notes in Computer Science, 639-655. DOI: http://dx.doi.org/10.1007/978-3-030-01246-5_38Google ScholarDigital Library
- Natacha Ruchaud and Jean-Luc Dugelay. 2016. Privacy protecting, intelligibility preserving video surveillance. IEEE International Conference on Multimedia & Expo Workshops (ICMEW). DOI: http://dx.doi.org/10.1109/icmew.2016.7574750Google Scholar
- Tao Li and Lei Lin. 2019. Anonymousnet: Natural face de-identification with measurable privacy. IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). DOI: http://dx.doi.org/10.1109/cvprw.2019.00013Google Scholar
- Jimmy Tekli, Bechara Al Bouna, Raphaël Couturier, Gilbert Tekli, Zeinab Al Zein, and Marc Kamradt. 2019. A Framework for Evaluating Image Obfuscation under Deep Learning-Assisted Privacy Attacks. 17th IEEE International Conference on Privacy, Security and Trust (PST). DOI: http://dx.doi.org/10.1109/pst47121.2019.8949040Google Scholar
- Natacha Ruchaud. 2015. Privacy protection filter using stegoscrambling in video surveillance. MediaEval - Wurzen, Germany: hal-01367560. DOI: https://hal.archives-ouvertes.fr/hal-01367560Google Scholar
- Ralph Gross, Edoardo Airoldi, Bradley Malin, and Latanya Sweeney. 2005. Integrating utility into face de-identification. International Workshop on Privacy Enhancing Technologies. Springer, Berlin, Heidelberg. Privacy Enhancing Technologies Lecture Notes in Computer Science (2006), 227-242. DOI: http://dx.doi.org/10.1007/11767831_15Google ScholarDigital Library
- Liang Du, Meng Yi, Erik Blasch, and Haibin Ling. 2014. GARP-face: Balancing privacy protection and utility preservation in face de-identification. IEEE International Joint Conference on Biometrics. DOI: http://dx.doi.org/10.1109/btas.2014.6996249Google Scholar
- Grigorios G. Chrysos and Stefanos Zafeiriou. 2017. Deep face deblurring. IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). DOI: http://dx.doi.org/10.1109/cvprw.2017.252Google Scholar
- Ziyi Shen, Wei-Sheng Lai, Tingfa Xu, Jan Kautz, and Ming-Hsuan Yang. 2018. Deep semantic face deblurring. Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition. DOI: http://dx.doi.org/10.1109/cvpr.2018.00862Google ScholarCross Ref
- Rakibul Hasan, Eman Hassan, Yifang Li, Kelly Caine, David J. Crandall, Roberto Hoyle, and Apu Kapadia. 2018. Viewer experience of obscuring scene elements in photos to enhance privacy. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. DOI: http://dx.doi.org/10.1145/3173574.3173621Google ScholarDigital Library
- Hanxiang Hao, David Güera, János Horváth, Amy R. Reibman, and Edward J. Delp. 2019. Robustness Analysis of Face Obscuration. arXiv preprint arXiv:1905.05243. DOI: https://arxiv.org/abs/1905.05243Google Scholar
- Pavel Korshunov and Touradj Ebrahimi. 2013. PEViD: privacy evaluation video dataset. Applications of Digital Image Processing XXXVI. Vol. 8856. International Society for Optics and Photonics. DOI: http://dx.doi.org/10.1117/12.2030974Google Scholar
- Hanxiang Hao, David Güera, Amy R. Reibman, and Edward J. Delp. 2019. A Utility-Preserving GAN for Face Obscuration. arXiv preprint arXiv:1906.11979. DOI: https://arxiv.org/abs/1906.11979Google Scholar
- Ankur Chattopadhyay. 2016. Developing an Innovative Framework for Design and Analysis of Privacy Enhancing Video Surveillance. Doctoral Dissertation. University of Colorado. DOI: http://hdl.handle.net/10976/166596Google Scholar
Recommendations
Privacy-enhancing technologies: approaches and development
In this paper, we discuss privacy threats on the Internet and possible solutions to this problem. Examples of privacy threats in the communication networks are identity disclosure, linking data traffic with identity, location disclosure in connection ...
Enhancing privacy in identity management systems
WPES '07: Proceedings of the 2007 ACM workshop on Privacy in electronic societyUser-privacy in existing identity management systems (IMS) can be improved.Indeed, private credential systems offer privacy enhancing capabilities not yet included in current IMS; e.g. proving claims such as age > 18, with age an attribute. This paper ...
P3P privacy enhancing agent
SWS '06: Proceedings of the 3rd ACM workshop on Secure web servicesProtecting personal privacy information is an inherently difficult problem. Privacy enhancing agents are software agents that help web users to protect their private information by collecting web site P3P [1] information and exchanging knowledge of web ...
Comments