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Robust classification methods predominantly concentrate on algorithms that address a specific threat model, resulting in ineffective defenses against other threat models.
Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
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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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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Evasion attacks against machine learning at test time
Battista Biggio, Igino Corona, Davide Maiorca, Blaine Nelson, Nedim Šrndić, Pavel Laskov, Giorgio Giacinto, and Fabio Roli · 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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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Anh Nguyen, Jason Yosinski, and Jeff Clune · 2015
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Adversarial machine learning at scale
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2016
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Adversarial examples are not easily detected: Bypassing ten detection methods
Nicholas Carlini and David Wagner · 2017
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A study and comparison of human and deep learning recognition performance under visual distortions
Samuel Dodge and Lina Karam · 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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Google’s cloud vision api is not robust to noise
Hossein Hosseini, Baicen Xiao, and Radha Poovendran · 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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Pixeldefend: Leveraging generative models to understand and defend against adversarial examples
Yang Song, Taesup Kim, Sebastian Nowozin, Stefano Ermon, and Nate Kushman · 2017
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Synthesizing robust adversarial examples
Anish Athalye, Logan Engstrom, Andrew Ilyas, and Kevin Kwok · 2018
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Unrestricted adversarial examples
Tom B Brown, Nicholas Carlini, Chiyuan Zhang, Catherine Olsson, Paul Christiano, and Ian Goodfellow · 2018
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Boosting adversarial attacks with momentum
Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, and Jianguo Li · 2018
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Harini Kannan, Alexey Kurakin, and Ian Goodfellow · 2018
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Adversarial examples in the physical world
Alexey Kurakin, Ian J Goodfellow, and Samy Bengio · 2018
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Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients
Andrew Ross and Finale Doshi-Velez · 2018
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Dogancan Temel and Ghassan AlRegib · 2018
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Cure-or: Challenging unreal and real environments for object recognition
Dogancan Temel, Jinsol Lee, and Ghassan AlRegib · 2018
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Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
Francesco Croce and Matthias Hein · 2020
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Uncovering the limits of adversarial training against norm-bounded adversarial examples
Sven Gowal, Chongli Qin, Jonathan Uesato, Timothy Mann, and Pushmeet Kohli · 2020
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Stochastic security: Adversarial defense using long-run dynamics of energy-based models
Mitch Hill, Jonathan Mitchell, and Song-Chun Zhu · 2020
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Perceptual adversarial robustness: Defense against unseen threat models
Cassidy Laidlaw, Sahil Singla, and Soheil Feizi · 2020
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Adversarial robustness against the union of multiple perturbation models
Pratyush Maini, Eric Wong, and Zico Kolter · 2020
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Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Madry · 2018
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Certified adversarial robustness via randomized smoothing
Jeremy Cohen, Elan Rosenfeld, and Zico Kolter · 2019
Cited alongside, same era.
Implicit generation and generalization in energy-based models
Yilun Du and Igor Mordatch · 2019
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Adversarial robustness as a prior for learned representations
Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Brandon Tran, and Aleksander Madry · 2019
Cited alongside, same era.
On the connection between adversarial robustness and saliency map interpretability
Christian Etmann, Sebastian Lunz, Peter Maass, and Carola-Bibiane Schönlieb · 2019
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Quantifying perceptual distortion of adversarial examples
Matt Jordan, Naren Manoj, Surbhi Goel, and Alexandros G Dimakis · 2019
Cited alongside, same era.
Testing robustness against unforeseen adversaries
Max Kaufmann, Daniel Kang, Yi Sun, Steven Basart, Xuwang Yin, Mantas Mazeika, Akul Arora, Adam Dziedzic, Franziska Boenisch, Tom Brown, et al · 2019
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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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Enhancing adversarial robustness via test-time transformation ensembling
Juan C Pérez, Motasem Alfarra, Guillaume Jeanneret, Laura Rueda, Ali Thabet, Bernard Ghanem, and Pablo Arbeláez · 2021
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Fixing data augmentation to improve adversarial robustness
Sylvestre-Alvise Rebuffi, Sven Gowal, Dan A Calian, Florian Stimberg, Olivia Wiles, and Timothy Mann · 2021
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Admix: Enhancing the transferability of adversarial attacks
Xiaosen Wang, Xuanran He, Jingdong Wang, and Kun He · 2021
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Attacking adversarial attacks as a defense
Boxi Wu, Heng Pan, Li Shen, Jindong Gu, Shuai Zhao, Zhifeng Li, Deng Cai, Xiaofei He, and Wei Liu · 2021
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Adversarial purification with score-based generative models
Jongmin Yoon, Sung Ju Hwang, and Juho Lee · 2021
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Threat model-agnostic adversarial defense using diffusion models
Tsachi Blau, Roy Ganz, Bahjat Kawar, Alex Bronstein, and Michael Elad · 2022
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Leo Schwinn, Leon Bungert, An Nguyen, René Raab, Falk Pulsmeyer, Doina Precup, Björn Eskofier, and Dario Zanca · 2022
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Investigating top-k white-box and transferable black-box attack
Chaoning Zhang, Philipp Benz, Adil Karjauv, Jae Won Cho, Kang Zhang, and In So Kweon · 2022
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A light recipe to train robust vision transformers
Edoardo Debenedetti, Vikash Sehwag, and Prateek Mittal · 2023
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Learning to transform dynamically for better adversarial transferability
Rongyi Zhu, Zeliang Zhang, Susan Liang, Zhuo Liu, and Chenliang Xu · 2024
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