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Large organizations such as social media companies continually release data, for example user images.
Labeled faces in the wild: A database forstudying face recognition in unconstrained environments
Huang, G. B., Mattar, M., Berg, T., and Learned-Miller, E · 2008
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 security of machine learning
Barreno, M., Nelson, B., Joseph, A. D., and Tygar, J. D · 2010
Earlier work this paper cites.
Poisoning Attacks against Support Vector Machines
Biggio, B., Nelson, B., and Laskov, P · 2012
Earlier work this paper cites.
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K. and Zisserman, A · 2014
Earlier work this paper cites.
DeepFace: Closing the Gap to Human-Level Performance in Face Verification
Taigman, Y., Yang, M., Ranzato, M., and Wolf, L · 2014
Earlier work this paper cites.
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., 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., Berg, A. C., and Fei-Fei, L · 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.
Is Feature Selection Secure against Training Data Poisoning?
Xiao, H., Biggio, B., Brown, G., Fumera, G., Eckert, C., and Roli, F · 2015
Earlier work this paper cites.
Densely Connected Convolutional Networks
Huang, G., Liu, Z., van der Maaten, L., and Weinberger, K. Q · 2016
Earlier work this paper cites.
Frontal to profile face verification in the wild
Sengupta, S., Chen, J.-C., Castillo, C., Patel, V. M., Chellappa, R., and Jacobs, D. W · 2016
Earlier work this paper cites.
Stealing machine learning models via prediction apis
Tramèr, F., Zhang, F., Juels, A., Reiter, M. K., and Ristenpart, T · 2016
Earlier work this paper cites.
Know you at one glance: A compact vector representation for low-shot learning
Cheng, Y., Zhao, J., Wang, Z., Xu, Y., Jayashree, K., Shen, S., and Feng, J · 2017
Cited alongside, same era.
Towards Deep Learning Models Resistant to Adversarial Attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2017
Cited alongside, same era.
Agedb: the first manually collected, in-the-wild age database
Moschoglou, S., Papaioannou, A., Sagonas, C., Deng, J., Kotsia, I., and Zafeiriou, S · 2017
Cited alongside, same era.
Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization
Muñoz-González, L., Biggio, B., Demontis, A., Paudice, A., Wongrassamee, V., Lupu, E. C., and Roli, F · 2017
Cited alongside, same era.
Embedding watermarks into deep neural networks
Uchida, Y., Nagai, Y., Sakazawa, S., and Satoh, S · 2017
Cited alongside, same era.
Vggface2: A dataset for recognising faces across pose and age
Data Poisoning against Differentially-Private Learners: Attacks and Defenses
Ma, Y., Zhu, X., and Hsu, J · 2019
Later among the works it cites.
Prediction poisoning: Towards defenses against dnn model stealing attacks
Orekondy, T., Schiele, B., and Fritz, M · 2019
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TensorClog: An Imperceptible Poisoning Attack on Deep Neural Network Applications
Shen, J., Zhu, X., and Ma, D · 2019
Later among the works it cites.
Transferable Clean-Label Poisoning Attacks on Deep Neural Nets
Zhu, C., Huang, W. R., Shafahi, A., Li, H., Taylor, G., Studer, C., and Goldstein, T · 2019
Later among the works it cites.
Language models are few-shot learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
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Cao, Q., Shen, L., Xie, W., Parkhi, O. M., and Zisserman, A · 2018
Cited alongside, same era.
Detecting backdoor attacks on deep neural networks by activation clustering
Chen, B., Carvalho, W., Baracaldo, N., Ludwig, H., Edwards, B., Lee, T., Molloy, I., and Srivastava, B · 2018
Cited alongside, same era.
Stronger Data Poisoning Attacks Break Data Sanitization Defenses
Koh, P. W., Steinhardt, J., and Liang, P · 2018
Cited alongside, same era.
MobileNetV2: Inverted Residuals and Linear Bottlenecks
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C · 2018
Cited alongside, same era.
Spectral Signatures in Backdoor Attacks
Tran, B., Li, J., and Madry, A · 2018
Cited alongside, same era.
Cosface: Large margin cosine loss for deep face recognition
Wang, H., Wang, Y., Zhou, Z., Ji, X., Gong, D., Zhou, J., Li, Z., and Liu, W · 2018
Cited alongside, same era.
Understanding generalization through visualizations
Huang, W. R., Emam, Z., Goldblum, M., Fowl, L., Terry, J. K., Huang, F., and Goldstein, T · 2019
Cited alongside, same era.
Later among the works it cites.
Witches’ brew: Industrial scale data poisoning via gradient matching
Geiping, J., Fowl, L., Huang, W. R., Czaja, W., Taylor, G., Moeller, M., and Goldstein, T · 2020
Later among the works it cites.
Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses
Goldblum, M., Tsipras, D., Xie, C., Chen, X., Schwarzschild, A., Song, D., Madry, A., Li, B., and Goldstein, T · 2020
Later among the works it cites.
The secretive company that might end privacy as we know it, Jan 2020
Hill, K · 2020
Later among the works it cites.
On the Effectiveness of Mitigating Data Poisoning Attacks with Gradient Shaping
Hong, S., Chandrasekaran, V., Kaya, Y., Dumitraş, T., and Papernot, N · 2020
Later among the works it cites.
MetaPoison: Practical General-purpose Clean-label Data Poisoning
Huang, W. R., Geiping, J., Fowl, L., Taylor, G., and Goldstein, T · 2020
Later among the works it cites.
Deep k-NN Defense against Clean-label Data Poisoning Attacks
Peri, N., Gupta, N., Huang, W. R., Fowl, L., Zhu, C., Feizi, S., Goldstein, T., and Dickerson, J. P · 2020
Later among the works it cites.
Lowkey: Leveraging adversarial attacks to protect social media users from facial recognition
Cherepanova, V., Goldblum, M., Foley, H., Duan, S., Dickerson, J., Taylor, G., and Goldstein, T · 2021
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