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Neural networks are susceptible to adversarial examples-small input perturbations that cause models to fail.
Visualizing data using t-sne
L. Van der Maaten and G. Hinton · 2008
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Learning multiple layers of features from tiny images
A. Krizhevsky, G. Hinton, et al · 2009
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Mnist handwritten digit database
Y. LeCun, C. Cortes, and C. Burges · 2010
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Evasion attacks against machine learning at test time
B. Biggio, I. Corona, D. Maiorca, B. Nelson, N. Šrndić, P. Laskov, G. Giacinto, and F. Roli · 2013
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Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2013
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Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2015
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Explaining and harnessing adversarial examples, 2015
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2015
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Deep residual learning for image recognition, 2015
K. He, X. Zhang, S. Ren, and J. Sun · 2015
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Distillation as a defense to adversarial perturbations against deep neural networks, 2015
N. Papernot, P. McDaniel, X. Wu, S. Jha, and A. Swami · 2015
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Defensive distillation is not robust to adversarial examples, 2016
N. Carlini and D. Wagner · 2016
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Overcoming catastrophic forgetting in neural networks, 2016
J. Kirkpatrick, R. Pascanu, N. Rabinowitz, J. Veness, G. Desjardins, A. A. Rusu, K. Milan, J. Quan, T. Ramalho, A. Grabska-Barwinska, D. Hassabis, C. Clopath, D. Kumaran, and R. Hadsell · 2016
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Making deep neural networks robust to label noise: a loss correction approach, 2016
G. Patrini, A. Rozza, A. Menon, R. Nock, and L. Qu · 2016
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Adversarial examples are not easily detected: Bypassing ten detection methods, 2017
N. Carlini and D. Wagner · 2017
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Detecting adversarial samples from artifacts, 2017
R. Feinman, R. R. Curtin, S. Shintre, and A. B. Gardner · 2017
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Towards crafting text adversarial samples, 2017
S. Samanta and S. Mehta · 2017
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Active learning for convolutional neural networks: A core-set approach, 2017
O. Sener and S. Savarese · 2017
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Audio adversarial examples: Targeted attacks on speech-to-text, 2018
N. Carlini and D. Wagner · 2018
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Adversarial attacks and defences: A survey, 2018
A. Chakraborty, M. Alam, V. Dey, A. Chattopadhyay, and D. Mukhopadhyay · 2018
Cited alongside, same era.
Shield: Fast, practical defense and vaccination for deep learning using jpeg compression, 2018
N. Das, M. Shanbhogue, S.-T. Chen, F. Hohman, S. Li, L. Chen, M. E. Kounavis, and D. H. Chau · 2018
Cited alongside, same era.
Adversarial logit pairing, 2018
H. Kannan, A. Kurakin, and I. Goodfellow · 2018
Cited alongside, same era.
The taboo trap: Behavioural detection of adversarial samples
I. Shumailov, Y. Zhao, R. Mullins, and R. Anderson · 2018
Cited alongside, same era.
Attacks meet interpretability: Attribute-steered detection of adversarial samples, 2018
G. Tao, S. Ma, Y. Liu, and X. Zhang · 2018
Cited alongside, same era.
Robustness may be at odds with accuracy, 2018
Continuous safety verification of neural networks, 2020
C.-H. Cheng and R. Yan · 2020
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Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks, 2020
F. Croce and M. Hein · 2020
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Benchmarking adversarial robustness on image classification
Y. Dong, Q.-A. Fu, X. Yang, T. Pang, H. Su, Z. Xiao, and J. Zhu · 2020
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Structural analysis and optimization of convolutional neural networks with a small sample size
R. N. D’souza, P.-Y. Huang, and F.-C. Yeh · 2020
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Github - locuslab/fast_adversarial: [iclr 2020] a repository for extremely fast adversarial training using fgsm, Jul 2020
locuslab · 2020
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Fast is better than free: Revisiting adversarial training, 2020
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D. Tsipras, S. Santurkar, L. Engstrom, A. Turner, and A. Madry · 2018
Cited alongside, same era.
Semantic redundancies in image-classification datasets: The 10% you don’t need, 2019
V. Birodkar, H. Mobahi, and S. Bengio · 2019
Cited alongside, same era.
Is ami (attacks meet interpretability) robust to adversarial examples?, 2019
N. Carlini · 2019
Cited alongside, same era.
On evaluating adversarial robustness
N. Carlini, A. Athalye, N. Papernot, W. Brendel, J. Rauber, D. Tsipras, I. J. Goodfellow, A. Madry, and A. Kurakin · 2019
Cited alongside, same era.
The efficacy of shield under different threat models, 2019
C. Cornelius, N. Das, S.-T. Chen, L. Chen, M. E. Kounavis, and D. H. Chau · 2019
Cited alongside, same era.
Adversarial examples are not bugs, they are features, 2019
A. Ilyas, S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry · 2019
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks, 2019
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu · 2019
Cited alongside, same era.
E. Wong, L. Rice, and J. Z. Kolter · 2020
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Searching to exploit memorization effect in learning with noisy labels
Q. Yao, H. Yang, B. Han, G. Niu, and J. T.-Y. Kwok · 2020
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Blackbox attacks on reinforcement learning agents using approximated temporal information
Y. Zhao, I. Shumailov, H. Cui, X. Gao, R. Mullins, and R. Anderson · 2020
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Bad characters: Imperceptible nlp attacks, 2021
N. Boucher, I. Shumailov, R. Anderson, and N. Papernot · 2021
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Adversarial policies: Attacking deep reinforcement learning, 2021
A. Gleave, M. Dennis, C. Wild, N. Kant, S. Levine, and S. Russell · 2021
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Unsolved problems in ml safety, 2021
D. Hendrycks, N. Carlini, J. Schulman, and J. Steinhardt · 2021
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Pervasive label errors in test sets destabilize machine learning benchmarks, 2021
C. G. Northcutt, A. Athalye, and J. Mueller · 2021
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Understanding the error in evaluating adversarial robustness, 2021
P. Xia, Z. Li, H. Niu, and B. Li · 2021
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Pipe overflow: Smashing voice authentication for fun and profit, 2022
S. Ahmed, Y. Wani, A. S. Shamsabadi, M. Yaghini, I. Shumailov, N. Papernot, and K. Fawaz · 2022
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Active label cleaning for improved dataset quality under resource constraints
M. Bernhardt, D. C. Castro, R. Tanno, A. Schwaighofer, K. C. Tezcan, M. Monteiro, S. Bannur, M. P. Lungren, A. Nori, B. Glocker, J. Alvarez-Valle, and O. Oktay · 2022
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On the limitations of stochastic pre-processing defenses, 2022
Y. Gao, I. Shumailov, K. Fawaz, and N. Papernot · 2022
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