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There is growing concern about image privacy due to the popularity of social media and photo devices, along with increasing use of face recognition systems.
Integrating utility into face de-identification
Ralph Gross, Edoardo Airoldi, Bradley Malin, and Latanya Sweeney · 2005
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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Model-based face de-identification
Ralph Gross, Latanya Sweeney, Fernando De la Torre, and Simon Baker · 2006
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Face de-identification
Ralph Gross, Latanya Sweeney, Jeffrey Cohn, Fernando De la Torre, and Simon Baker · 2009
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Differential identifiability
Jaewoo Lee and Chris Clifton · 2012
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Membership privacy: A unifying framework for privacy definitions
Ninghui Li, Wahbeh Qardaji, Dong Su, Yi Wu, and Weining Yang · 2013
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Rappor: Randomized aggregatable privacy-preserving ordinal response
Úlfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
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Defeating image obfuscation with deep learning
Richard McPherson, Reza Shokri, and Vitaly Shmatikov · 2016
Earlier work this paper cites.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Earlier work this paper cites.
Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2016
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Privbayes: Private data release via bayesian networks
Jun Zhang, Graham Cormode, Cecilia M Procopiuc, Divesh Srivastava, and Xiaokui Xiao · 2017
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Machine learning models that remember too much
Congzheng Song, Thomas Ristenpart, and Vitaly Shmatikov · 2017
Cited alongside, same era.
Image pixelization with differential privacy
Liyue Fan · 2018
Cited alongside, same era.
Learning to anonymize faces for privacy preserving action detection
Zhongzheng Ren, Yong Jae Lee, and Michael S Ryoo · 2018
Cited alongside, same era.
Natural and effective obfuscation by head inpainting
Qianru Sun, Liqian Ma, Seong Joon Oh, Luc Van Gool, Bernt Schiele, and Mario Fritz · 2018
Cited alongside, same era.
Pate-gan: Generating synthetic data with differential privacy guarantees
James Jordon, Jinsung Yoon, and Mihaela van der Schaar · 2018
Cited alongside, same era.
Differentially private mixture of generative neural networks
Gergely Acs, Luca Melis, Claude Castelluccia, and Emiliano De Cristofaro · 2018
Cited alongside, same era.
Practical image obfuscation with provable privacy
Liyue Fan · 2019
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AnonymousNet: Natural face de-identification with measurable privacy
Tao Li and Lei Lin · 2019
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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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Learning to synthesize and manipulate natural images
Jun-Yan Zhu and Jim Foley · 2019
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Dp-cgan: Differentially private synthetic data and label generation
Reihaneh Torkzadehmahani, Peter Kairouz, and Benedict Paten · 2019
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Fooling automated surveillance cameras: adversarial patches to attack person detection
Simen Thys, Wiebe Van Ranst, and Toon Goedemé · 2019
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Differential privacy synthetic data challenge, 2018
2018
Cited alongside, same era.
A hybrid model for identity obfuscation by face replacement
Qianru Sun, Ayush Tewari, Weipeng Xu, Mario Fritz, Christian Theobalt, and Bernt Schiele · 2018
Cited alongside, same era.
Privacy-protective-gan for face de-identification
Yifan Wu, Fan Yang, and Haibin Ling · 2018
Cited alongside, same era.
Poison frogs! targeted clean-label poisoning attacks on neural networks
Ali Shafahi, W Ronny Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer, Tudor Dumitras, and Tom Goldstein · 2018
Cited alongside, same era.
Optimizing the latent space of generative networks
Piotr Bojanowski, Armand Joulin, David Lopez-Paz, and Arthur Szlam · 2018
Cited alongside, same era.
The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
Cited alongside, same era.
Making an invisibility cloak: Real world adversarial attacks on object detectors
Zuxuan Wu, Ser-Nam Lim, Larry Davis, and Tom Goldstein · 2019
Later among the works it cites.
Transferable clean-label poisoning attacks on deep neural nets
Chen Zhu, W. Ronny Huang, Hengduo Li, Gavin Taylor, Christoph Studer, and Tom Goldstein · 2019
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Large scale GAN training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2019
Later among the works it cites.
Interpreting the latent space of gans for semantic face editing
Yujun Shen, Jinjin Gu, Xiaoou Tang, and Bolei Zhou · 2019
Later among the works it cites.
A practical method to reduce privacy loss when disclosing statistics based on small samples
Raj Chetty and John Friedman · 2019
Later among the works it cites.
DeepBlur: A simple and effective method for natural image obfuscation
Tao Li and Min Soo Choi · 2021
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