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Deep neural networks have been shown to be fooled rather easily using adversarial attack algorithms.
Dimensionality reduction by learning an invariant mapping
Raia Hadsell, Sumit Chopra, and Yann LeCun · 2006
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
How to explain individual classification decisions
David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, and Klaus-Robert MÞller · 2010
Earlier work this paper cites.
Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. 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.
Densenet: Implementing efficient convnet descriptor pyramids
Forrest N. Iandola, Matthew W. Moskewicz, Sergey Karayev, Ross B. Girshick, Trevor Darrell, and Kurt Keutzer · 2014
Earlier work this paper cites.
Object detectors emerge in deep scene cnns
Bolei Zhou, Aditya Khosla, Àgata Lapedriza, Aude Oliva, and Antonio Torralba · 2015
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.
Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2016
Earlier work this paper cites.
Adversarial machine learning at scale
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2016
Earlier work this paper 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
Earlier work this paper cites.
Learning deep features for discriminative localization
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba · 2016
Cited alongside, same era.
Adversarial patch
Tom B Brown, Dandelion Mané, Aurko Roy, Martín Abadi, and Justin Gilmer · 2017
Cited alongside, same era.
Adversarial examples are not easily detected: Bypassing ten detection methods
Nicholas Carlini and David A. Wagner · 2017
Cited alongside, same era.
Accountability of ai under the law: The role of explanation
Finale Doshi-Velez, Mason Kortz, Ryan Budish, Chris Bavitz, Sam Gershman, David O’Brien, Stuart Schieber, James Waldo, David Weinberger, and Alexandra Wood · 2017
Cited alongside, same era.
Interpretation of neural networks is fragile
Amirata Ghorbani, Abubakar Abid, and James Zou · 2017
Cited alongside, same era.
Robust physical adversarial attack on faster r-cnn object detector
Shang-Tse Chen, Cory Cornelius, Jason Martin, and Duen Horng Chau · 2018
Closest in time.
Opportunities and obstacles for deep learning in biology and medicine
Travers Ching, Daniel S Himmelstein, Brett K Beaulieu-Jones, Alexandr A Kalinin, Brian T Do, Gregory P Way, Enrico Ferrero, Paul-Michael Agapow, Michael Zietz, Michael M Hoffman, et al · 2018
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Lavan: Localized and visible adversarial noise
Danny Karmon, Daniel Zoran, and Yoav Goldberg · 2018
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Tell me where to look: Guided attention inference network
Kunpeng Li, Ziyan Wu, Kuan-Chuan Peng, Jan Ernst, and Yun Fu · 2018
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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Pieter-Jan Kindermans, Sara Hooker, Julius Adebayo, Maximilian Alber, Kristof T Schütt, Sven Dähne, Dumitru Erhan, and Been Kim · 2017
Cited alongside, same era.
A comprehensive study of security of internet-of-things
Arsalan Mosenia and Niraj K Jha · 2017
Cited alongside, same era.
Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Cited alongside, same era.
Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
Cited alongside, same era.
Sanity checks for saliency maps
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian J. Goodfellow, Moritz Hardt, and Been Kim · 2018
Cited alongside, same era.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Anish Athalye, Nicholas Carlini, and David A. Wagner · 2018
Cited alongside, same era.
Synthesizing robust adversarial examples
Anish Athalye, Logan Engstrom, Andrew Ilyas, and Kevin Kwok · 2018
Cited alongside, same era.
Darts: Deceiving autonomous cars with toxic signs
Chawin Sitawarin, Arjun Nitin Bhagoji, Arsalan Mosenia, Mung Chiang, and Prateek Mittal · 2018
Closest in time.
Physical adversarial examples for object detectors
Dawn Song, Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Florian Tramèr, Atul Prakash, and Tadayoshi Kohno · 2018
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Adversarial framing for image and video classification
Michał Zając, Konrad Żołna, Negar Rostamzadeh, and Pedro O Pinheiro · 2018
Closest in time.
Adversarial attacks on medical machine learning
Samuel G. Finlayson, John D. Bowers, Joichi Ito, Jonathan L. Zittrain, Andrew L. Beam, and Isaac S. Kohane · 2019
Closest in time.
Fooling neural network interpretations via adversarial model manipulation
Juyeon Heo, Sunghwan Joo, and Taesup Moon · 2019
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One pixel attack for fooling deep neural networks
Jiawei Su, Danilo Vasconcellos Vargas, and Kouichi Sakurai · 2019
Closest in time.