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Adversarial training is the de facto most promising defense against adversarial examples.
Instrumental variables
Roger J Bowden and Darrell A Turkington · 1990
Earlier work this paper cites.
Webvision: the organization of the retina and visual system
Helga Kolb, Eduardo Fernandez, and Ralph Nelson · 1995
Earlier work this paper cites.
An introduction to instrumental variables for epidemiologists
Sander Greenland · 2000
Earlier work this paper cites.
The fundamental plan of the retina
Richard H Masland · 2001
Earlier work this paper cites.
Causality
Judea Pearl · 2009
Earlier work this paper cites.
Retinotopic organization of human ventral visual cortex
Michael J Arcaro, Stephanie A McMains, Benjamin D Singer, and Sabine Kastner · 2009
Earlier work this paper cites.
The remarkable, yet not extraordinary, human brain as a scaled-up primate brain and its associated cost
Suzana Herculano-Houzel · 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.
Instrumental variable methods for causal inference
Michael Baiocchi, Jing Cheng, and Dylan S Small · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
Earlier work this paper cites.
Deepfool: a simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2016
Earlier work this paper cites.
Adversarial examples in the physical world, 2016
Alexey Kurakin, Ian Goodfellow, Samy Bengio, et al · 2016
Earlier work this paper cites.
The limitations of deep learning in adversarial settings
Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z Berkay Celik, and Ananthram Swami · 2016
Earlier work this paper cites.
Causal inference in statistics: A primer
Judea Pearl, Madelyn Glymour, and Nicholas P Jewell · 2016
Earlier work this paper cites.
Control function instrumental variable estimation of nonlinear causal effect models
Zijian Guo and Dylan S Small · 2016
Earlier work this paper cites.
Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, 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.
Sergey Zagoruyko and Nikos Komodakis · 2016
Earlier work this paper cites.
Adversarial examples for semantic segmentation and object detection
Cihang Xie, Jianyu Wang, Zhishuai Zhang, Yuyin Zhou, Lingxi Xie, and Alan Yuille · 2017
Earlier work this paper cites.
Houdini: Fooling deep structured prediction models
Moustapha Cisse, Yossi Adi, Natalia Neverova, and Joseph Keshet · 2017
Earlier work this paper cites.
Adversarial attacks on neural network policies
Sandy Huang, Nicolas Papernot, Ian Goodfellow, Yan Duan, and Pieter Abbeel · 2017
Earlier work this paper cites.
Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
Earlier work this paper cites.
Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models
Pin-Yu Chen, Huan Zhang, Yash Sharma, Jinfeng Yi, and Cho-Jui Hsieh · 2017
Earlier work this paper cites.
Universal adversarial perturbations
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, and Pascal Frossard · 2017
Earlier work this paper cites.
beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
Earlier work this paper cites.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Anish Athalye, Nicholas Carlini, and David Wagner · 2018
Earlier work this paper cites.
Adversarial logit pairing
Harini Kannan, Alexey Kurakin, and Ian Goodfellow · 2018
Cited alongside, same era.
Ensemble adversarial training: Attacks and defenses
Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel · 2018
Cited alongside, same era.
Adversarial spheres
Justin Gilmer, Luke Metz, Fartash Faghri, Samuel S Schoenholz, Maithra Raghu, Martin Wattenberg, and Ian Goodfellow · 2018
Cited alongside, same era.
Adversarially robust generalization requires more data
Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, and Aleksander Madry · 2018
Cited alongside, same era.
Audio adversarial examples: Targeted attacks on speech-to-text
Nicholas Carlini and David Wagner · 2018
Cited alongside, same era.
mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2018
Cited alongside, same era.
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
Francesco Croce and Matthias Hein · 2020
Later among the works it cites.
On adaptive attacks to adversarial example defenses
Florian Tramer, Nicholas Carlini, Wieland Brendel, and Aleksander Madry · 2020
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Learnable boundary guided adversarial training
Jiequan Cui, Shu Liu, Liwei Wang, and Jiaya Jia · 2020
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Adversarial examples improve image recognition
Cihang Xie, Mingxing Tan, Boqing Gong, Jiang Wang, Alan L Yuille, and Quoc V Le · 2020
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Do adversarially robust imagenet models transfer better?
Hadi Salman, Andrew Ilyas, Logan Engstrom, Ashish Kapoor, and Aleksander Madry · 2020
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Thermometer encoding: One hot way to resist adversarial examples
Jacob Buckman, Aurko Roy, Colin Raffel, and Ian Goodfellow · 2018
Cited alongside, same era.
The Book of Why: The New Science of Cause and Effect
Judea Pearl and Dana Mackenzie · 2018
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks, 2018
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
Cited alongside, same era.
Decision-based adversarial attacks: Reliable attacks against black-box machine learning models
Wieland Brendel, Jonas Rauber, and Matthias Bethge · 2018
Cited alongside, same era.
Mitigating adversarial effects through randomization
Cihang Xie, Jianyu Wang, Zhishuai Zhang, Zhou Ren, and Alan Yuille · 2018
Cited alongside, same era.
Defense-gan: Protecting classifiers against adversarial attacks using generative models
Pouya Samangouei, Maya Kabkab, and Rama Chellappa · 2018
Cited alongside, same era.
Modeling biological immunity to adversarial examples
Edward Kim, Jocelyn Rego, Yijing Watkins, and Garrett T Kenyon · 2020
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Api-net: Robust generative classifier via a single discriminator
Xinshuai Dong, Hong Liu, Rongrong Ji, Liujuan Cao, Qixiang Ye, Jianzhuang Liu, and Qi Tian · 2020
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A causal view on robustness of neural networks
Cheng Zhang, Kun Zhang, and Yingzhen Li · 2020
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Jason A. Roy, 2020
2020
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Adversarial attacks and defenses in deep learning
Kui Ren, Tianhang Zheng, Zhan Qin, and Xue Liu · 2020
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Fundamental tradeoffs between invariance and sensitivity to adversarial perturbations
Florian Tramer, Jens Behrmann, Nicholas Carlini, Nicolas Papernot, and Jorn-Henrik Jacobsen · 2020
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Enhancing intrinsic adversarial robustness via feature pyramid decoder
Guanlin Li, Shuya Ding, Jun Luo, and Chang Liu · 2020
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Understanding adversarial examples from the mutual influence of images and perturbations
Chaoning Zhang, Philipp Benz, Tooba Imtiaz, and In So Kweon · 2020
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Biologically inspired mechanisms for adversarial robustness
Manish Vuyyuru Reddy, Andrzej Banburski, Nishka Pant, and Tomaso Poggio · 2020
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Rain: A simple approach for robust and accurate image classification networks
Jiawei Du, Hanshu Yan, Vincent YF Tan, Joey Tianyi Zhou, Rick Siow Mong Goh, and Jiashi Feng · 2020
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Towards achieving adversarial robustness by enforcing feature consistency across bit planes
Sravanti Addepalli, Arya Baburaj, Gaurang Sriramanan, and R Venkatesh Babu · 2020
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Randaugment: Practical automated data augmentation with a reduced search space
Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le · 2020
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Square attack: a query-efficient black-box adversarial attack via random search
Maksym Andriushchenko, Francesco Croce, Nicolas Flammarion, and Matthias Hein · 2020
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Minimally distorted adversarial examples with a fast adaptive boundary attack
Francesco Croce and Matthias Hein · 2020
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Understanding the robustness of skeleton-based action recognition under adversarial attack
He Wang, Feixiang He, Zhexi Peng, Yong-Liang Yang, Tianjia Shao, Kun Zhou, and David Hogg · 2021
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Stabilized medical image attacks
Gege Qi, Lijun Gong, Yibing Song, Kai Ma, and Yefeng Zheng · 2021
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Basar:black-box attack on skeletal action recognition
Yunfeng Diao, Tianjia Shao, Yong-Liang Yang, Kun Zhou, and He Wang · 2021
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Adversarial robustness through the lens of causality
Yonggang Zhang, Mingming Gong, Tongliang Liu, Gang Niu, Xinmei Tian, Bo Han, Bernhard Schölkopf, and Kun Zhang · 2021
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Learning under adversarial and interventional shifts
Harvineet Singh, Shalmali Joshi, Finale Doshi-Velez, and Himabindu Lakkaraju · 2021
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Bag of tricks for adversarial training
Tianyu Pang, Xiao Yang, Yinpeng Dong, Hang Su, and Jun Zhu · 2021
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Causalvae: disentangled representation learning via neural structural causal models
Mengyue Yang, Furui Liu, Zhitang Chen, Xinwei Shen, Jianye Hao, and Jun Wang · 2021
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