Fetching the paper…
Reading the bibliography…
There has been extensive research on developing defense techniques against adversarial attacks; however, they have been mainly designed for specific model families or application domains, therefore, they cannot be easily extended.
Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces
Rainer Storn and Kenneth Price · 1997
Earlier work this paper cites.
Ensemble learning via negative correlation
Yong Liu and Xin Yao · 1999
Earlier work this paper cites.
Simultaneous training of negatively correlated neural networks in an ensemble
Yong Liu and Xin Yao · 1999
Earlier work this paper cites.
Ensemble methods in machine learning
Thomas G. Dietterich · 2000
Earlier work this paper cites.
A constructive algorithm for training cooperative neural network ensembles
Md M Islam, Xin Yao, and Kazuyuki Murase · 2003
Earlier work this paper cites.
A constructive algorithm for training cooperative neural network ensembles
Md.M. Islam, Xin Yao, and K. Murase · 2003
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
Earlier work this paper cites.
MNIST handwritten digit database
Yann LeCun and Corinna Cortes · 2010
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.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
Earlier work this paper cites.
Foveation-based mechanisms alleviate adversarial examples
Yan Luo, Xavier Boix, Gemma Roig, Tomaso Poggio, and Qi Zhao · 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 · 2015
Earlier work this paper cites.
Adversarial feature selection against evasion attacks
Fei Zhang, Patrick PK Chan, Battista Biggio, Daniel S Yeung, and Fabio Roli · 2015
Earlier work this paper cites.
A study of the effect of jpg compression on adversarial images
Gintare Karolina Dziugaite, Zoubin Ghahramani, and Daniel M Roy · 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.
Adversarial machine learning at scale
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2016
Earlier work this paper cites.
Distillation as a defense to adversarial perturbations against deep neural networks
N. Papernot, P. McDaniel, X. Wu, S. Jha, and A. Swami · 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.
Nikos Komodakis Sergey Zagoruyko · 2016
Earlier work this paper cites.
Random feature nullification for adversary resistant deep architecture
Qinglong Wang, Wenbo Guo, Kaixuan Zhang, Xinyu Xing, C Lee Giles, and Xue Liu · 2016
Earlier work this paper cites.
Achieving human parity in conversational speech recognition
Wayne Xiong, Jasha Droppo, Xuedong Huang, Frank Seide, Mike Seltzer, Andreas Stolcke, Dong Yu, and Geoffrey Zweig · 2016
Earlier work this paper cites.
Synthesizing robust adversarial examples
Anish Athalye, Logan Engstrom, Andrew Ilyas, and Kevin Kwok · 2017
Earlier work this paper cites.
Magnet and “efficient defenses against adversarial attacks” are not robust to adversarial examples
Nicholas Carlini and David Wagner · 2017
Cited alongside, same era.
Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
Cited alongside, same era.
Parseval networks: Improving robustness to adversarial examples
Moustapha Cisse, Piotr Bojanowski, Edouard Grave, Yann Dauphin, and Nicolas Usunier · 2017
Cited alongside, same era.
Keeping the bad guys out: Protecting and vaccinating deep learning with jpeg compression
Nilaksh Das, Madhuri Shanbhogue, Shang-Tse Chen, Fred Hohman, Li Chen, Michael E Kounavis, and Duen Horng Chau · 2017
Cited alongside, same era.
Discovering adversarial examples with momentum
Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Xiaolin Hu, and Jun Zhu · 2017
Harini Kannan, Alexey Kurakin, and Ian Goodfellow · 2018
Later among the works it cites.
Defense against adversarial attacks using high-level representation guided denoiser
Fangzhou Liao, Ming Liang, Yinpeng Dong, Tianyu Pang, Xiaolin Hu, and Jun Zhu · 2018
Later among the works it cites.
Towards robust neural networks via random self-ensemble
Xuanqing Liu, Minhao Cheng, Huan Zhang, and Cho-Jui Hsieh · 2018
Later among the works it cites.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
Later among the works it cites.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Xavier Gastaldi · 2017
Cited alongside, same era.
On the (statistical) detection of adversarial examples
Kathrin Grosse, Praveen Manoharan, Nicolas Papernot, Michael Backes, and Patrick McDaniel · 2017
Cited alongside, same era.
Countering adversarial images using input transformations
Chuan Guo, Mayank Rana, Moustapha Cisse, and Laurens Van Der Maaten · 2017
Cited alongside, same era.
Adversarial example defense: Ensembles of weak defenses are not strong
Warren He, James Wei, Xinyun Chen, Nicholas Carlini, and Dawn Song · 2017
Cited alongside, same era.
Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2017
Cited alongside, same era.
No need to worry about adversarial examples in object detection in autonomous vehicles
Jiajun Lu, Hussein Sibai, Evan Fabry, and David Forsyth · 2017
Cited alongside, same era.
Magnet: a two-pronged defense against adversarial examples
Dongyu Meng and Hao Chen · 2017
Cited alongside, same era.
Maria-Irina Nicolae, Mathieu Sinn, Minh Ngoc Tran, Beat Buesser, Ambrish Rawat, Martin Wistuba, Valentina Zantedeschi, Nathalie Baracaldo, Bryant Chen, Heiko Ludwig, Ian Molloy, and Ben Edwards · 2018
Later among the works it cites.
Technical report on the cleverhans v2.1.0 adversarial examples library
Nicolas Papernot · 2018
Later among the works it cites.
Understanding adversarial training: Increasing local stability of supervised models through robust optimization
Uri Shaham, Yutaro Yamada, and Sahand Negahban · 2018
Later among the works it cites.
Detecting adversarial examples through image transformation
Ying Cai Shixin Tian, Guolei Yang · 2018
Later among the works it cites.
On evaluating adversarial robustness
Nicholas Carlini, Anish Athalye, Nicolas Papernot, Wieland Brendel, Jonas Rauber, Dimitris Tsipras, Ian Goodfellow, and Aleksander Madry · 2019
Later among the works it cites.
Hopskipjumpattack: A query-efficient decision-based attack, 2019
Jianbo Chen, Michael I. Jordan, and Martin J. Wainwright · 2019
Later among the works it cites.
Certified adversarial robustness via randomized smoothing
Jeremy M. Cohen, Elan Rosenfeld, and J. Zico Kolter · 2019
Later among the works it cites.
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
Later among the works it cites.
Simple black-box adversarial attacks
Chuan Guo, Jacob R Gardner, Yurong You, Andrew Gordon Wilson, and Kilian Q Weinberger · 2019
Later among the works it cites.
Why deep-learning ais are so easy to fool
Douglas Heaven · 2019
Later among the works it cites.
Image super-resolution as a defense against adversarial attacks
Aamir Mustafa, Salman H Khan, Munawar Hayat, Jianbing Shen, and Ling Shao · 2019
Later among the works it cites.
Improving adversarial robustness via promoting ensemble diversity
Tianyu Pang, Kun Xu, Chao Du, Ning Chen, and Jun Zhu · 2019
Later among the works it cites.
Error correcting output codes improve probability estimation and adversarial robustness of deep neural networks
Gunjan Verma and Ananthram Swami · 2019
Later among the works it cites.
Image transformation based defense against adversarial perturbation on deep learning models
Akshay Agarwal, Richa Singh, Mayank Vatsa, and Nalini K. Ratha · 2020
Closest in time.
When nas meets robustness: In search of robust architectures against adversarial attacks
Minghao Guo, Yuzhe Yang, Rui Xu, Ziwei Liu, and Dahua Lin · 2020
Closest in time.
Sanchari Sen, Balaraman Ravindran, and Anand Raghunathan · 2020
Closest in time.
On adaptive attacks to adversarial example defenses
Florian Tramer, Nicholas Carlini, Wieland Brendel, and Aleksander Madry · 2020
Closest in time.