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From TV news to Google StreetView, face obscuration has been used for privacy protection.
Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression
Samarati, P. and Sweeney, L · 1998
Earlier work this paper cites.
Active appearance models
Cootes, T. F., Edwards, G. J., and Taylor, C. J · 2001
Earlier work this paper cites.
Poisson image editing
Pérez, P., Gangnet, M., and Blake, A · 2003
Earlier work this paper cites.
Best practices for convolutional neural networks applied to visual document analysis
Simard, P. Y., Steinkraus, D., and Platt, J. C · 2003
Earlier work this paper cites.
Integrating utility into face de-identification
Gross, R., Airoldo, E., Malin, B., and Sweeney, L · 2005
Earlier work this paper cites.
Preserving privacy by de-identifying face images
Newton, E. M., Sweeney, L., and Malin, B · 2005
Earlier work this paper cites.
Face swapping: Automatically replacing faces in photographs
Bitouk, D., Kumar, N., Dhillon, S., Belhumeur, P., and Nayar, S. K · 2008
Earlier work this paper cites.
Dlib-ml: A machine learning toolkit
King, D. E · 2009
Earlier work this paper cites.
Garp-face: Balancing privacy protection and utility preservation in face de-identification
Du, L., Yi, M., Blasch, E., and Ling, H · 2014
Earlier work this paper cites.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
Cited alongside, same era.
Conditional generative adversarial nets
Mirza, M. and Osindero, S · 2014
Cited alongside, same era.
A data-driven approach to cleaning large face datasets
Ng, H. and Winkler, S · 2014
Cited alongside, same era.
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L · 2015
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2015
Cited alongside, same era.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S · 2017
Later among the works it cites.
Fast face-swap using convolutional neural networks
Korshunova, I., Shi, W., Dambre, J., and Theis, L · 2017
Later among the works it cites.
Age progression/regression by conditional adversarial autoencoder
Zhang, Z., Song, Y., and Qi, H · 2017
Later among the works it cites.
Deepfake video detection using recurrent neural networks
Güera, D. and Delp, E. J · 2018
Later among the works it cites.
Attribute-guided face generation using conditional cyclegan
Lu, Y., Tai, Y.-W., and Tang, C.-K · 2018
Later among the works it cites.
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Learning to generate chairs, tables and cars with convolutional networks
Dosovitskiy, A., Springenberg, J. T., Tatarchenko, M., and Brox, T · 2016
Cited alongside, same era.
Perceptual losses for real-time style transfer and super-resolution
Johnson, J., Alahi, A., and Fei-Fei, L · 2016
Cited alongside, same era.
Defeating image obfuscation with deep learning
McPherson, R., Shokri, R., and Shmatikov, V · 2016
Cited alongside, same era.
Face aging with conditional generative adversarial networks
Antipov, G., Baccouche, M., and Dugelay, J.-L · 2017
Cited alongside, same era.
k-same-net: k-anonymity with generative deep neural networks for face deidentification
Meden, B., Ž. Emeršič, Štruc, V., and Peer, P · 2018
Later among the works it cites.
Natural and effective obfuscation by head inpainting
Sun, Q., Ma, L., Joon Oh, S., Gool, L. V., Schiele, B., and Fritz, M · 2018
Later among the works it cites.
Privacy-protective-gan for privacy preserving face de-identification
Wu, Y., Yang, F., Xu, Y., and Ling, H · 2019
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