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We outline a detection method for adversarial inputs to deep neural networks.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Strategies for training large scale neural network language models
Tomáš Mikolov, Anoop Deoras, Daniel Povey, Lukáš Burget, and Jan Černockỳ · 2011
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Online particle detection with neural networks based on topological calorimetry information
T Ciodaro, D Deva, JM De Seixas, and D Damazio · 2012
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Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
Geoffrey Hinton, Li Deng, Dong Yu, George E Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara N Sainath, et al · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Perseus: the persistent homology software
Vidit Nanda · 2012
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Learning hierarchical features for scene labeling
Clement Farabet, Camille Couprie, Laurent Najman, and Yann LeCun · 2013
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Connectomic reconstruction of the inner plexiform layer in the mouse retina
Moritz Helmstaedter, Kevin L Briggman, Srinivas C Turaga, Viren Jain, H Sebastian Seung, and Winfried Denk · 2013
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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Towards deep neural network architectures robust to adversarial examples
Shixiang Gu and Luca Rigazio · 2014
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Deep learning of the tissue-regulated splicing code
Michael KK Leung, Hui Yuan Xiong, Leo J Lee, and Brendan J Frey · 2014
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le · 2014
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Joint training of a convolutional network and a graphical model for human pose estimation
Jonathan J Tompson, Arjun Jain, Yann LeCun, and Christoph Bregler · 2014
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Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
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Learning with a strong adversary
Ruitong Huang, Bing Xu, Dale Schuurmans, and Csaba Szepesvári · 2015
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Convergent learning: Do different neural networks learn the same representations?
Matroid filtrations and computational persistent homology
Gregory Henselman and Robert Ghrist · 2016
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Adversarial examples detection in deep networks with convolutional filter statistics
Xin Li and Fuxin Li · 2016
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Universal adversarial perturbations
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, and Pascal Frossard · 2016
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Dimensionality reduction as a defense against evasion attacks on machine learning classifiers
Arjun Nitin Bhagoji, Daniel Cullina, and Prateek Mittal · 2017
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Adversarial examples are not easily detected: Bypassing ten detection methods
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Yixuan Li, Jason Yosinski, Jeff Clune, Hod Lipson, and John Hopcroft · 2015
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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Understanding neural networks through deep visualization
Jason Yosinski, Jeff Clune, Anh Nguyen, Thomas Fuchs, and Hod Lipson · 2015
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Tensorflow: Large-scale machine learning on heterogeneous distributed systems
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, et al · 2016
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Measuring neural net robustness with constraints
Osbert Bastani, Yani Ioannou, Leonidas Lampropoulos, Dimitrios Vytiniotis, Aditya Nori, and Antonio Criminisi · 2016
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Nicholas Carlini and David Wagner · 2017
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
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Intriguing properties of adversarial examples
Ekin D Cubuk, Barret Zoph, Samuel S Schoenholz, and Quoc V Le · 2017
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On detecting adversarial perturbations
Jan Hendrik Metzen, Tim Genewein, Volker Fischer, and Bastian Bischoff · 2017
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Dionysus
Dmitriy Morozov · 2017
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One pixel attack for fooling deep neural networks
Jiawei Su, Danilo Vasconcellos Vargas, and Sakurai Kouichi · 2017
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