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Machine learning algorithms are vulnerable to poisoning attacks, where a fraction of the training data is manipulated to degrade the algorithms' performance.
Poisoning Attacks with Generative Adversarial Nets
L. Muñoz-González, B. Pfitzner, M. Russo, J. Carnerero-Cano, and E. C. Lupu · 1906
Earlier work this paper cites.
Fast Exact Multiplication by the Hessian
B. A. Pearlmutter · 1994
Earlier work this paper cites.
Gradient-Based Learning Applied to Document Recognition
Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, et al · 1998
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Pattern Recognition and Machine Learning
C. M. Bishop · 2006
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Efficient multiple hyperparameter learning for log-linear models
C.-S. Foo, C. B. Do, and A. Y. Ng · 2008
Earlier work this paper cites.
Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation , volume 105
A. Griewank and A. Walther · 2008
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The security of machine learning
M. Barreno, B. Nelson, A. D. Joseph, and J. D. Tygar · 2010
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Adversarial Machine Learning
L. Huang, A. D. Joseph, B. Nelson, B. I. P. Rubinstein, and J. D. Tygar · 2011
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Sparse Algorithms Are Not Stable: A No-Free-Lunch Theorem
H. Xu, C. Caramanis, and S. Mannor · 2011
Earlier work this paper cites.
Poisoning Attacks against Support Vector Machines
B. Biggio, B. Nelson, and P. Laskov · 2012
Earlier work this paper cites.
Generic Methods for Optimization-Based Modeling
J. Domke · 2012
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Practical Bilevel Optimization: Algorithms and Applications , volume 30
J. F. Bard · 2013
Earlier work this paper cites.
Gradient-based Hyperparameter Optimization through Reversible Learning
D. Maclaurin, D. Duvenaud, and R. Adams · 2015
Cited alongside, same era.
Using Machine Teaching to Identify Optimal Training-Set Attacks on Machine Learners
S. Mei and X. Zhu · 2015
Cited alongside, same era.
ImageNet Large Scale Visual Recognition Challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al · 2015
Cited alongside, same era.
Is Feature Selection Secure against Training Data Poisoning?
H. Xiao, B. Biggio, G. Brown, G. Fumera, C. Eckert, and F. Roli · 2015
Cited alongside, same era.
Deep Learning
I. Goodfellow, Y. Bengio, and A. Courville · 2016
Cited alongside, same era.
Rethinking the Inception Architecture for Computer Vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2016
Cited alongside, same era.
Bilevel Programming for Hyperparameter Optimization and Meta-Learning
L. Franceschi, P. Frasconi, S. Salzo, R. Grazzi, and M. Pontil · 2018
Later among the works it cites.
Stronger Data Poisoning Attacks Break Data Sanitization Defenses
P. W. Koh, J. Steinhardt, and P. Liang · 2018
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Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection
A. Paudice, L. Muñoz-González, A. György, and E. C. Lupu · 2018
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Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks
A. Shafahi, W. R. Huang, M. Najibi, O. Suciu, C. Studer, T. Dumitras, and T. Goldstein · 2018
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Training Set Debugging Using Trusted Items
X. Zhang, X. Zhu, and S. Wright · 2018
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Forward and Reverse Gradient-Based Hyperparameter Optimization
L. Franceschi, M. Donini, P. Frasconi, and M. Pontil · 2017
Cited alongside, same era.
Understanding Black-box Predictions via Influence Functions
P. W. Koh and P. Liang · 2017
Cited alongside, same era.
Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization
L. Muñoz-González, B. Biggio, A. Demontis, A. Paudice, V. Wongrassamee, E. C. Lupu, and F. Roli · 2017
Cited alongside, same era.
Certified Defenses for Data Poisoning Attacks
J. Steinhardt, P. W. W. Koh, and P. S. Liang · 2017
Cited alongside, same era.
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
H. Xiao, K. Rasul, and R. Vollgraf · 2017
Cited alongside, same era.
Challenges and Advances in Adversarial Machine Learning
L. Muñoz-González, J. Carnerero-Cano, K. T. Co, and E. C. Lupu
Cited in the paper.
Sever: A Robust Meta-Algorithm for Stochastic Optimization
I. Diakonikolas, G. Kamath, D. Kane, J. Li, J. Steinhardt, and A. Stewart · 2019
Later among the works it cites.
Transferable Clean-Label Poisoning Attacks on Deep Neural Nets
C. Zhu, W. R. Huang, H. Li, G. Taylor, C. Studer, and T. Goldstein · 2019
Later among the works it cites.
On the Iteration Complexity of Hypergradient Computation
R. Grazzi, L. Franceschi, M. Pontil, and S. Salzo · 2020
Later among the works it cites.
MetaPoison: Practical General-purpose Clean-label Data Poisoning
W. R. Huang, J. Geiping, L. Fowl, G. Taylor, and T. Goldstein · 2020
Later among the works it cites.
Witches’ Brew: Industrial Scale Data Poisoning via Gradient Matching
J. Geiping, L. Fowl, W. R. Huang, W. Czaja, G. Taylor, M. Moeller, and T. Goldstein · 2021
Closest in time.
Deep Partition Aggregation: Provable Defense against General Poisoning Attacks
A. Levine and S. Feizi · 2021
Closest in time.