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Machine learning is vulnerable to adversarial manipulation.
Exploring randomly wired neural networks for image recognition, 2019
S. Xie, A. Kirillov, R. Girshick, and K. He · 1904
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Learning multiple layers of features from tiny images
A. Krizhevsky · 2009
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Learning multiple layers of features from tiny images
A. Krizhevsky, G. Hinton, et al · 2009
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Poisoning attacks against support vector machines
B. Biggio, B. Nelson, and P. Laskov · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition
J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel · 2012
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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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Traffic sign recognition - how far are we from the solution?
M. Mathias, R. Timofte, R. Benenson, and L. V. Gool · 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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Keras, 2015
F. Chollet et al · 2015
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Explaining and harnessing adversarial examples, 2015
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2015
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Dex: Deep expectation of apparent age from a single image
R. Rothe, R. Timofte, and L. V. Gool · 2015
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
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Towards the science of security and privacy in machine learning
N. Papernot, P. McDaniel, A. Sinha, and M. Wellman · 2016
Cited alongside, same era.
Decision-based adversarial attacks: Reliable attacks against black-box machine learning models, 2017
W. Brendel, J. Rauber, and M. Bethge · 2017
Cited alongside, same era.
Towards evaluating the robustness of neural networks
N. Carlini and D. Wagner · 2017
Cited alongside, same era.
Badnets: Identifying vulnerabilities in the machine learning model supply chain
T. Gu, B. Dolan-Gavitt, and S. Garg · 2017
Cited alongside, same era.
A unified approach to interpreting model predictions
S. M. Lundberg and S. Lee · 2017
Cited alongside, same era.
Weight agnostic neural networks, 2019
A. Gaier and D. Ha · 2019
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Darts: Differentiable architecture search
H. Liu, K. Simonyan, and Y. Yang · 2019
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Neural cleanse: Identifying and mitigating backdoor attacks in neural networks
B. Wang, Y. Yao, S. Shan, H. Li, B. Viswanath, H. Zheng, and B. Y. Zhao · 2019
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Huggingface’s transformers: State-of-the-art natural language processing
T. Wolf, L. Debut, V. Sanh, J. Chaumond, C. Delangue, A. Moi, P. Cistac, T. Rault, R. Louf, M. Funtowicz, et al · 2019
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Dynamic backdoor attacks against machine learning models, 2020
A. Salem, R. Wen, M. Backes, S. Ma, and Y. Zhang · 2020
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Methods for interpreting and understanding deep neural networks
G. Montavon, W. Samek, and K. Müller · 2017
Cited alongside, same era.
Practical black-box attacks against machine learning
N. Papernot, P. McDaniel, I. Goodfellow, S. Jha, Z. B. Celik, and A. Swami · 2017
Cited alongside, same era.
Membership inference attacks against machine learning models
R. Shokri, M. Stronati, C. Song, and V. Shmatikov · 2017
Cited alongside, same era.
Learning transferable architectures for scalable image recognition
B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le · 2017
Cited alongside, same era.
Wild patterns: Ten years after the rise of adversarial machine learning
B. Biggio and F. Roli · 2018
Cited alongside, same era.
On the effectiveness of interval bound propagation for training verifiably robust models
S. Gowal, K. Dvijotham, R. Stanforth, R. Bunel, C. Qin, J. Uesato, R. Arandjelovic, T. Mann, and P. Kohli · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Y. Zhao, D. Wang, X. Gao, R. Mullins, P. Lio, and M. Jamnik · 2020
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Blind backdoors in deep learning models
E. Bagdasaryan and V. Shmatikov · 2021
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Bad character injection: Imperceptible attacks on NLP models
N. Boucher, I. Shumailov, N. Papernot, and R. Anderson · 2021
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Rethinking image-scaling attacks: The interplay between vulnerabilities in machine learning systems, 2021
Y. Gao, I. Shumailov, and K. Fawaz · 2021
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Handcrafted backdoors in deep neural networks
S. Hong, N. Carlini, and A. Kurakin · 2021
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Deeppayload: Black-box backdoor attack on deep learning models through neural payload injection
Y. Li, J. Hua, H. Wang, C. Chen, and Y. Liu · 2021
Later among the works it cites.
Planting undetectable backdoors in machine learning models, 2022
S. Goldwasser, M. P. Kim, V. Vaikuntanathan, and O. Zamir · 2022
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