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Machine learning models are vulnerable to adversarial examples: small changes to images can cause computer vision models to make mistakes such as identifying a school bus as an ostrich.
Cortical correlate of pattern backward masking
GYULA KovAcs, Rufin Vogels, and Guy A Orban · 1995
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Information processing strategies and pathways in the primate visual system
D. C. Van Essen and C. H Anderson · 1995
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Hierarchical models of object recognition in cortex
Maximilian Riesenhuber and Tomaso Poggio · 1999
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Combining sensory information: mandatory fusion within, but not between, senses
James M Hillis, Marc O Ernst, Martin S Banks, and Michael S Landy · 2002
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Metamers of the ventral stream
Jeremy Freeman and Eero P Simoncelli · 2011
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Visual perception and saccadic eye movements
Michael Ibbotson and Bart Krekelberg · 2011
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Animal eyes
Michael F Land and Dan-Eric Nilsson · 2012
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Evasion attacks against machine learning at test time
Battista Biggio, Igino Corona, Davide Maiorca, Blaine Nelson, Nedim Srndic, Pavel Laskov, Giorgio Giacinto, and Fabio Roli · 2013
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20 years of learning about vision: Questions answered, questions unanswered, and questions not yet asked
Bruno A Olshausen · 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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Deep neural networks rival the representation of primate it cortex for core visual object recognition
Charles F Cadieu, Ha Hong, Daniel LK Yamins, Nicolas Pinto, Diego Ardila, Ethan A Solomon, Najib J Majaj, and James J DiCarlo · 2014
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Deep gaze i: Boosting saliency prediction with feature maps trained on imagenet
Matthias Kümmerer, Lucas Theis, and Matthias Bethge · 2014
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Detecting meaning in rsvp at 13 ms per picture
Mary C Potter, Brad Wyble, Carl Erick Hagmann, and Emily S McCourt · 2014
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A neural algorithm of artistic style
Leon A Gatys, Alexander S Ecker, and Matthias Bethge · 2015
Cited alongside, same era.
The limitations of deep learning in adversarial settings
Nicolas Papernot, Patrick D. McDaniel, Somesh Jha, Matt Fredrikson, Z. Berkay Celik, and Ananthram Swami · 2015
Cited alongside, same era.
Rethinking the Inception Architecture for Computer Vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2015
Cited alongside, same era.
Identity Mappings in Deep Residual Networks
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Adversarial Machine Learning at Scale
A. Kurakin, I. Goodfellow, and S. Bengio · 2016
Cited alongside, same era.
Adversarial examples in the physical world
Tom B Brown, Dandelion Mané, Aurko Roy, Martín Abadi, and Justin Gilmer · 2017
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Humans, but not deep neural networks, often miss giant targets in scenes
Miguel P Eckstein, Kathryn Koehler, Lauren E Welbourne, and Emre Akbas · 2017
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Comparing deep neural networks against humans: object recognition when the signal gets weaker
Robert Geirhos, David HJ Janssen, Heiko H Schütt, Jonas Rauber, Matthias Bethge, and Felix A Wichmann · 2017
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Attacking machine learning with adversarial examples, 2017
Ian Goodfellow, Nicolas Papernot, Sandy Huang, Yan Duan, Pieter Abbeel, and Jack Clark · 2017
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Adversarial examples for malware detection
Kathrin Grosse, Nicolas Papernot, Praveen Manoharan, Michael Backes, and Patrick D. McDaniel · 2017
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Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2016
Cited alongside, same era.
Delving into transferable adversarial examples and black-box attacks
Yanpei Liu, Xinyun Chen, Chang Liu, and Dawn Song · 2016
Cited alongside, same era.
Deep learning models of the retinal response to natural scenes
Lane McIntosh, Niru Maheswaranathan, Aran Nayebi, Surya Ganguli, and Stephen Baccus · 2016
Cited alongside, same era.
Transferability in machine learning: from phenomena to black-box attacks using adversarial samples
Nicolas Papernot, Patrick McDaniel, and Ian Goodfellow · 2016
Cited alongside, same era.
Distillation as a defense to adversarial perturbations against deep neural networks
Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami · 2016
Cited alongside, same era.
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Alemi · 2016
Cited alongside, same era.
Using goal-driven deep learning models to understand sensory cortex
Daniel L. K. Yamins and James J. DiCarlo · 2016
Cited alongside, same era.
Neuroscience-inspired artificial intelligence
Demis Hassabis, Dharshan Kumaran, Christopher Summerfield, and Matthew Botvinick · 2017
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Provable defenses against adversarial examples via the convex outer adversarial polytope
J Zico Kolter and Eric Wong · 2017
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Deepgaze ii: Predicting fixations from deep features over time and tasks
Matthias Kümmerer, Tom Wallis, and Matthias Bethge · 2017
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
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Practical black-box attacks against machine learning
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Berkay Celik, and Ananthram Swami · 2017
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Ensemble Adversarial Training: Attacks and Defenses
F. Tramèr, A. Kurakin, N. Papernot, D. Boneh, and P. McDaniel · 2017
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Feature squeezing: Detecting adversarial examples in deep neural networks
Weilin Xu, David Evans, and Yanjun Qi · 2017
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Thermometer encoding: One hot way to resist adversarial examples
Jacob Buckman, Aurko Roy, Colin Raffel, and Ian Goodfellow · 2018
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Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks
Rishi Rajalingham, Elias B. Issa, Pouya Bashivan, Kohitij Kar, Kailyn Schmidt, and James J DiCarlo · 2018
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