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State-of-the-art Deep Neural Networks can be easily fooled into providing incorrect high-confidence predictions for images with small amounts of adversarial noise.
“Gradient-based learning applied to document recognition,”
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, · 1998
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“Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods,”
John C Platt, · 1999
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“Imagenet classification with deep convolutional neural networks,”
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton, · 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
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“Explaining and harnessing adversarial examples,”
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy, · 2014
Earlier work this paper cites.
“Foundations of data science,”
John Hopcroft and Ravi Kannan, · 2014
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“Very deep convolutional networks for large-scale image recognition,”
K. Simonyan and A. Zisserman, · 2015
Cited alongside, same era.
“Deep residual learning for image recognition,”
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, · 2015
Cited alongside, same era.
“Dropout as a Bayesian approximation: Representing model uncertainty in deep learning,”
Yarin Gal and Zoubin Ghahramani, · 2015
Cited alongside, same era.
“Deep neural networks are easily fooled: High confidence predictions for unrecognizable images,”
Anh Nguyen, Jason Yosinski, and Jeff Clune, · 2015
Cited alongside, same era.
“ImageNet Large Scale Visual Recognition Challenge,”
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei, · 2015
Cited alongside, same era.
“Towards open set deep networks,”
Abhijit Bendale and Terrance E Boult, · 2016
Later among the works it cites.
“Deepfool: a simple and accurate method to fool deep neural networks,”
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard, · 2016
Later among the works it cites.
“Universal adversarial perturbations,”
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, and Pascal Frossard, · 2016
Later among the works it cites.
“Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition,”
Mahmood Sharif, Sruti Bhagavatula, Lujo Bauer, and Michael K. Reiter, · 2016
Later among the works it cites.
“Improving the robustness of deep neural networks via stability training,”
Stephan Zheng, Yang Song, Thomas Leung, and Ian Goodfellow, · 2016
Later among the works it cites.
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