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Face obfuscation (blurring, mosaicing, etc.) has been shown to be effective for privacy protection; nevertheless, object recognition research typically assumes access to complete, unobfuscated images.
MMDetection: Open mmlab detection toolbox and benchmark
Chen, K., Wang, J., Pang, J., Cao, Y., Xiong, Y., Li, X., Sun, S., Feng, W., Liu, Z., Xu, J., Zhang, Z., Cheng, D., Zhu, C., Cheng, T., Zhao, Q., Li, B., Lu, X., Zhu, R., Wu, Y., Dai, J., Wang, J., Shi, J., Ouyang, W., Loy, C. C., and Lin, D · 1906
Earlier work this paper cites.
WordNet: An electronic lexical database
Miller, G. A · 1998
Earlier work this paper cites.
People identification with limited labels in privacy-protected video
Chang, Y., Yan, R., Chen, D., and Yang, J · 2006
Earlier work this paper cites.
Privacy-preserving logistic regression
Chaudhuri, K. and Monteleoni, C · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Earlier work this paper cites.
Large-scale privacy protection in google street view
Frome, A., Cheung, G., Abdulkader, A., Zennaro, M., Wu, B., Bissacco, A., Adam, H., Neven, H., and Vincent, L · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
Earlier work this paper cites.
The pascal visual object classes (voc) challenge
Everingham, M., Van Gool, L., Williams, C. K., Winn, J., and Zisserman, A · 2010
Earlier work this paper cites.
Multiparty differential privacy via aggregation of locally trained classifiers
Pathak, M. A., Rane, S., and Raj, B · 2010
Earlier work this paper cites.
Sun database: Large-scale scene recognition from abbey to zoo
Xiao, J., Hays, J., Ehinger, K. A., Oliva, A., and Torralba, A · 2010
Earlier work this paper cites.
Vision based intelligent traffic management system
Malhi, M. H., Aslam, M. H., Saeed, F., Javed, O., and Fraz, M · 2011
Earlier work this paper cites.
Privacy in pharmacogenetics: An end-to-end case study of personalized warfarin dosing
Fredrikson, M., Lantz, E., Jha, S., Lin, S., Page, D., and Ristenpart, T · 2014
Earlier work this paper cites.
Microsoft coco: Common objects in context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., and Zitnick, C. L · 2014
Earlier work this paper cites.
2014 internet trends, May 2014
Meeker, M · 2014
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The privacy-utility tradeoff for remotely teleoperated robots
Butler, D. J., Huang, J., Roesner, F., and Cakmak, M · 2015
Earlier work this paper cites.
Towards privacy-preserving recognition of human activities
Dai, J., Saghafi, B., Wu, J., Konrad, J., and Ishwar, P · 2015
Earlier work this paper cites.
Model inversion attacks that exploit confidence information and basic countermeasures
Fredrikson, M., Jha, S., and Ristenpart, T · 2015
Earlier work this paper cites.
Fast r-cnn
Girshick, R · 2015
Earlier work this paper cites.
Faster r-cnn: Towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R., and Sun, J · 2015
Earlier work this paper cites.
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., et al · 2015
Earlier work this paper cites.
Privacy-preserving deep learning
Shokri, R. and Shmatikov, V · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2015
Earlier work this paper cites.
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A · 2015
Earlier work this paper cites.
Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Yu, F., Seff, A., Zhang, Y., Song, S., Funkhouser, T., and Xiao, J · 2015
Earlier work this paper cites.
Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy
Gilad-Bachrach, R., Dowlin, N., Laine, K., Lauter, K., Naehrig, M., and Wernsing, J · 2016
Earlier work this paper cites.
Learning privately from multiparty data
Hamm, J., Cao, Y., and Belkin, M · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 0.5 mb model size
Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J., and Keutzer, K · 2016
Earlier work this paper cites.
Defeating image obfuscation with deep learning
McPherson, R., Shokri, R., and Shmatikov, V · 2016
Earlier work this paper cites.
Faceless person recognition: Privacy implications in social media
Oh, S. J., Benenson, R., Fritz, M., and Schiele, B · 2016
Cited alongside, same era.
Oblivious multi-party machine learning on trusted processors
Ohrimenko, O., Schuster, F., Fournet, C., Mehta, A., Nowozin, S., Vaswani, K., and Costa, M · 2016
Cited alongside, same era.
Privacy-preserving human activity recognition from extreme low resolution
Ryoo, M. S., Rothrock, B., Fleming, C., and Yang, H. J · 2016
Cited alongside, same era.
Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2016
Cited alongside, same era.
Examining the impact of blur on recognition by convolutional networks
Vasiljevic, I., Chakrabarti, A., and Shakhnarovich, G · 2016
Cited alongside, same era.
Natural and effective obfuscation by head inpainting
Sun, Q., Ma, L., Joon Oh, S., Van Gool, L., Schiele, B., and Fritz, M · 2018
Later among the works it cites.
Slalom: Fast, verifiable and private execution of neural networks in trusted hardware
Tramer, F. and Boneh, D · 2018
Later among the works it cites.
Towards privacy-preserving visual recognition via adversarial training: A pilot study
Wu, Z., Wang, Z., Wang, Z., and Jin, H · 2018
Later among the works it cites.
Shufflenet: An extremely efficient convolutional neural network for mobile devices
Zhang, X., Zhou, X., Lin, M., and Sun, J · 2018
Later among the works it cites.
Low latency privacy preserving inference
Brutzkus, A., Gilad-Bachrach, R., and Elisha, O · 2019
Later among the works it cites.
The secret sharer: Evaluating and testing unintended memorization in neural networks
Carlini, N., Liu, C., Erlingsson, Ú., Kos, J., and Song, D · 2019
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Zagoruyko, S. and Komodakis, N · 2016
Cited alongside, same era.
Practical secure aggregation for privacy-preserving machine learning
Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., Ramage, D., Segal, A., and Seth, K · 2017
Cited alongside, same era.
Minimax filter: learning to preserve privacy from inference attacks
Hamm, J · 2017
Cited alongside, same era.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H · 2017
Cited alongside, same era.
Densely connected convolutional networks
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q · 2017
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2017
Cited alongside, same era.
Effectiveness and users’ experience of obfuscation as a privacy-enhancing technology for sharing photos
Li, Y., Vishwamitra, N., Knijnenburg, B. P., Hu, H., and Caine, K · 2017
Cited alongside, same era.
Later among the works it cites.
Dulhanty, C. and Wong, A · 2019
Later among the works it cites.
Practical image obfuscation with provable privacy
Fan, L · 2019
Later among the works it cites.
Deepobfuscator: Adversarial training framework for privacy-preserving image classification
Li, A., Guo, J., Yang, H., and Chen, Y · 2019
Later among the works it cites.
A world with a billion cameras watching you is just around the corner, December 2019
Lin, L. and Purnell, N · 2019
Later among the works it cites.
Exploiting unintended feature leakage in collaborative learning
Melis, L., Song, C., De Cristofaro, E., and Shmatikov, V · 2019
Later among the works it cites.
Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning
Nasr, M., Shokri, R., and Houmansadr, A · 2019
Later among the works it cites.
Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al · 2019
Later among the works it cites.
Mnasnet: Platform-aware neural architecture search for mobile
Tan, M., Chen, B., Pang, R., Vasudevan, V., Sandler, M., Howard, A., and Le, Q. V · 2019
Later among the works it cites.
Privacy protection in street-view panoramas using depth and multi-view imagery
Uittenbogaard, R., Sebastian, C., Vijverberg, J., Boom, B., Gavrila, D. M., et al · 2019
Later among the works it cites.
P3sgd: Patient privacy preserving sgd for regularizing deep cnns in pathological image classification
Wu, B., Zhao, S., Sun, G., Zhang, X., Su, Z., Zeng, C., and Liu, Z · 2019
Later among the works it cites.
Ensei: Efficient secure inference via frequency-domain homomorphic convolution for privacy-preserving visual recognition
Bian, S., Wang, T., Hiromoto, M., Shi, Y., and Sato, T · 2020
Later among the works it cites.
nuscenes: A multimodal dataset for autonomous driving
Caesar, H., Bankiti, V., Lang, A. H., Vora, S., Liong, V. E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., and Beijbom, O · 2020
Later among the works it cites.
Extracting training data from large language models
Carlini, N., Tramer, F., Wallace, E., Jagielski, M., Herbert-Voss, A., Lee, K., Roberts, A., Brown, T., Song, D., Erlingsson, U., et al · 2020
Later among the works it cites.
Membership inference attacks on sequence-to-sequence models: Is my data in your machine translation system?
Hisamoto, S., Post, M., and Duh, K · 2020
Later among the works it cites.
Federated learning: Challenges, methods, and future directions
Li, T., Sahu, A. K., Talwalkar, A., and Smith, V · 2020
Later among the works it cites.
Data and its (dis) contents: A survey of dataset development and use in machine learning research
Paullada, A., Raji, I. D., Bender, E. M., Denton, E., and Hanna, A · 2020
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Avid dataset: Anonymized videos from diverse countries
Piergiovanni, A. and Ryoo, M · 2020
Later among the works it cites.
Raspberry pi assisted face recognition framework for enhanced law-enforcement services in smart cities
Sajjad, M., Nasir, M., Muhammad, K., Khan, S., Jan, Z., Sangaiah, A. K., Elhoseny, M., and Baik, S. W · 2020
Later among the works it cites.
Evade deep image retrieval by stashing private images in the hash space
Xiao, Y., Wang, C., and Gao, X · 2020
Later among the works it cites.
Towards fairer datasets: Filtering and balancing the distribution of the people subtree in the imagenet hierarchy
Yang, K., Qinami, K., Fei-Fei, L., Deng, J., and Russakovsky, O · 2020
Later among the works it cites.
Pass: An imagenet replacement for self-supervised pretraining without humans
Asano, Y., Rupprecht, C., Zisserman, A., and Vedaldi, A · 2021
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Can you fake it until you make it? impacts of differentially private synthetic data on downstream classification fairness
Cheng, V., Suriyakumar, V. M., Dullerud, N., Joshi, S., and Ghassemi, M · 2021
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Large image datasets: A pyrrhic win for computer vision?
Prabhu, V. U. and Birhane, A · 2021
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