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The unprecedented success of deep neural networks in many applications has made these networks a prime target for adversarial exploitation.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
The MNIST database of handwritten digits [http://yann.lecun.com/exdb/mnist/index.html], 1998
Yann LeCun, Corinna Cortes, and Christopher J.C. Burges · 1998
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Earlier work this paper cites.
Spatial transformer networks
Max Jaderberg, Karen Simonyan, Andrew Zisserman, et al · 2015
Earlier work this paper cites.
Deep speech 2: End-to-end speech recognition in english and mandarin
Dario Amodei, Sundaram Ananthanarayanan, Rishita Anubhai, Jingliang Bai, Eric Battenberg, Carl Case, Jared Casper, Bryan Catanzaro, Qiang Cheng, Guoliang Chen, et al · 2016
Earlier work this paper cites.
End to end learning for self-driving cars
Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, et al · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
End-to-end training of deep visuomotor policies
S Levine, C Finn, T Darrell, and P Abbeel · 2016
Cited alongside, same era.
Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
Cited alongside, same era.
Badnets: Identifying vulnerabilities in the machine learning model supply chain
Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Garg · 2017
Cited alongside, same era.
Trojaning attack on neural networks
Yingqi Liu, Shiqing Ma, Yousra Aafer, Wen-Chuan Lee, Juan Zhai, Weihang Wang, and Xiangyu Zhang · 2017
Cited alongside, same era.
Neural trojans
Yuntao Liu, Yang Xie, and Ankur Srivastava · 2017
Cited alongside, same era.
Universal adversarial perturbations
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, and Pascal Frossard · 2017
Cited alongside, same era.
Backdoor embedding in convolutional neural network models via invisible perturbation
Cong Liao, Haoti Zhong, Anna Squicciarini, Sencun Zhu, and David Miller · 2018
Later among the works it cites.
Fine-pruning: Defending against backdooring attacks on deep neural networks
Kang Liu, Brendan Dolan-Gavitt, and Siddharth Garg · 2018
Later among the works it cites.
Poison frogs! targeted clean-label poisoning attacks on neural networks
Ali Shafahi, W Ronny Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer, Tudor Dumitras, and Tom Goldstein · 2018
Later among the works it cites.
Clean-label backdoor attacks
Alexander Turner, Dimitris Tsipras, and Aleksander Madry · 2018
Later among the works it cites.
https://tiny-imagenet.herokuapp.com/
Tiny ImageNet · 2019
Closest in time.
Strip: A defence against trojan attacks on deep neural networks
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Towards poisoning of deep learning algorithms with back-gradient optimization
Luis Muñoz-González, Battista Biggio, Ambra Demontis, Andrea Paudice, Vasin Wongrassamee, Emil C Lupu, and Fabio Roli · 2017
Cited alongside, same era.
Certified defenses for data poisoning attacks
Jacob Steinhardt, Pang Wei W Koh, and Percy S Liang · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Detecting backdoor attacks on deep neural networks by activation clustering
Bryant Chen, Wilka Carvalho, Nathalie Baracaldo, Heiko Ludwig, Benjamin Edwards, Taesung Lee, Ian Molloy, and Biplav Srivastava · 2018
Cited alongside, same era.
Stronger data poisoning attacks break data sanitization defenses
Pang Wei Koh, Jacob Steinhardt, and Percy Liang · 2018
Cited alongside, same era.
When does machine learning
Octavian Suciu, Radu Marginean, Yigitcan Kaya, Hal Daume III, and Tudor Dumitras
Cited in the paper.
Yansong Gao, Chang Xu, Derui Wang, Shiping Chen, Damith C Ranasinghe, and Surya Nepal · 2019
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Machine learning in energy economics and finance: A review
Hamed Ghoddusi, Germán G Creamer, and Nima Rafizadeh · 2019
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Deep transfer learning for few-shot sar image classification
Mohammad Rostami, Soheil Kolouri, Eric Eaton, and Kyungnam Kim · 2019
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Applications of artificial neural networks in health care organizational decision-making: A scoping review
Nida Shahid, Tim Rappon, and Whitney Berta · 2019
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Gotta catch’em all: Using concealed trapdoors to detect adversarial attacks on neural networks
Shawn Shan, Emily Willson, Bolun Wang, Bo Li, Haitao Zheng, and Ben Y Zhao · 2019
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Neural cleanse: Identifying and mitigating backdoor attacks in neural networks
Bolun Wang, Yuanshun Yao, Shawn Shan, Huiying Li, Bimal Viswanath, Haitao Zheng, and Ben Y Zhao · 2019
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