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We question the current evaluation practice on diffusion-based purification methods.
Imagenet: A large-scale hierarchical image database
Jia Deng, R. Socher, Li Fei-Fei, Wei Dong, Kai Li, and Li-Jia Li · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2010
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, A. Bissacco, Bo Wu, and A. Ng · 2011
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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Narain Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2011
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Deep residual learning for image recognition
Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun · 2015
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Deepfool: A simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2015
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Sergey Zagoruyko and Nikos Komodakis · 2016
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Synthesizing robust adversarial examples
Anish Athalye, Logan Engstrom, Andrew Ilyas, and Kevin Kwok · 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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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Anish Athalye, Nicholas Carlini, and David Wagner · 2018
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Defense-gan: Protecting classifiers against adversarial attacks using generative models
Pouya Samangouei, Maya Kabkab, and Rama Chellappa · 2018
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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 · 2019
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Minimally distorted adversarial examples with a fast adaptive boundary attack
Francesco Croce and Matthias Hein · 2019
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Robustness (python library), 2019
Logan Engstrom, Andrew Ilyas, Shibani Santurkar, and Dimitris Tsipras · 2019
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Me-net: Towards effective adversarial robustness with matrix estimation
Yuzhe Yang, Guo Zhang, Dina Katabi, and Zhi Xu · 2019
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Theoretically principled trade-off between robustness and accuracy
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P. Xing, Laurent El Ghaoui, and Michael I. Jordan · 2019
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Adversarial robustness on in- and out-distribution improves explainability
Maximilian Augustin, Alexander Meinke, and Matthias Hein · 2020
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Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alex Nichol · 2021
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Improving robustness using generated data
Sven Gowal, Sylvestre-Alvise Rebuffi, Olivia Wiles, Florian Stimberg, Dan Andrei Calian, and Timothy Mann · 2021
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Exploring architectural ingredients of adversarially robust deep neural networks
Hanxun Huang, Yisen Wang, Sarah Monazam Erfani, Quanquan Gu, James Bailey, and Xingjun Ma · 2021
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Stable neural ode with lyapunov-stable equilibrium points for defending against adversarial attacks
Qiyu Kang, Yang Song, Qinxu Ding, and Wee Peng Tay · 2021
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Fast minimum-norm adversarial attacks through adaptive norm constraints
Maura Pintor, Fabio Roli, Wieland Brendel, and Battista Biggio · 2021
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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 A. 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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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and P. Abbeel · 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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Fast is better than free: Revisiting adversarial training
Eric Wong, Leslie Rice, and J. Zico Kolter · 2020
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Adaptive checkpoint adjoint method for gradient estimation in neural ode
Juntang Zhuang, Nicha C. Dvornek, Xiaoxiao Li, Sekhar Chandra Tatikonda, Xenophon Papademetris, and James S. Duncan · 2020
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Robust learning meets generative models: Can proxy distributions improve adversarial robustness?
Vikash Sehwag, Saeed Mahloujifar, Tinashe Handina, Sihui Dai, Chong Xiang, Mung Chiang, and Prateek Mittal · 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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Evaluating the adversarial robustness of adaptive test-time defenses
Francesco Croce, Sven Gowal, Thomas Brunner, Evan Shelhamer, Matthias Hein, and Taylan Cemgil · 2022
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DISCO: Adversarial defense with local implicit functions
Chih-Hui Ho and Nuno Vasconcelos · 2022
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Diffusion models for adversarial purification
Weili Nie, Brandon Guo, Yujia Huang, Chaowei Xiao, Arash Vahdat, and Anima Anandkumar · 2022
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Robustness and accuracy could be reconcilable by (proper) definition
Tianyu Pang, Min Lin, Xiao Yang, Junyi Zhu, and Shuicheng Yan · 2022
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Reducing excessive margin to achieve a better accuracy vs. robustness trade-off
Rahul Rade and Seyed-Mohsen Moosavi-Dezfooli · 2022
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Guided diffusion model for adversarial purification
Jinyi Wang, Zhaoyang Lyu, Dahua Lin, Bo Dai, and Hongfei Fu · 2022
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