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Diffusion-based purification defenses leverage diffusion models to remove crafted perturbations of adversarial examples and achieve state-of-the-art robustness.
Estimation of non-normalized statistical models by score matching
Aapo Hyvärinen and Peter Dayan · 2005
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
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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
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
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Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Sergey Zagoruyko and Nikos Komodakis · 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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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
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The reversible residual network: Backpropagation without storing activations
Aidan N Gomez, Mengye Ren, Raquel Urtasun, and Roger B Grosse · 2017
Earlier work this paper cites.
Practical black-box attacks against machine learning
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Berkay Celik, and Ananthram Swami · 2017
Earlier work this paper cites.
Pixeldefend: Leveraging generative models to understand and defend against adversarial examples
Yang Song, Taesup Kim, Sebastian Nowozin, Stefano Ermon, and Nate Kushman · 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.
Synthesizing robust adversarial examples
Anish Athalye, Logan Engstrom, Andrew Ilyas, and Kevin Kwok · 2018
Earlier work this paper cites.
Reversible architectures for arbitrarily deep residual neural networks
Bo Chang, Lili Meng, Eldad Haber, Lars Ruthotto, David Begert, and Elliot Holtham · 2018
Earlier work this paper cites.
The total variation distance between high-dimensional gaussians
Luc Devroye, Abbas Mehrabian, and Tommy Reddad · 2018
Earlier work this paper cites.
Robust physical-world attacks on deep learning visual classification
Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Chaowei Xiao, Atul Prakash, Tadayoshi Kohno, and Dawn Song · 2018
Earlier work this paper cites.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
Earlier work this paper cites.
Logit pairing methods can fool gradient-based attacks
Marius Mosbach, Maksym Andriushchenko, Thomas Trost, Matthias Hein, and Dietrich Klakow · 2018
Earlier work this paper cites.
Defense-gan: Protecting classifiers against adversarial attacks using generative models
Pouya Samangouei, Maya Kabkab, and Rama Chellappa · 2018
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Adversarial risk and the dangers of evaluating against weak attacks
Jonathan Uesato, Brendan O’Donoghue, Pushmeet Kohli, and Aäron van den Oord · 2018
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Implicit generation and modeling with energy based models
Yilun Du and Igor Mordatch · 2019
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Your classifier is secretly an energy based model and you should treat it like one
Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, and Kevin Swersky · 2019
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Deepsec: A uniform platform for security analysis of deep learning model
Xiang Ling, Shouling Ji, Jiaxu Zou, Jiannan Wang, Chunming Wu, Bo Li, and Ting Wang · 2019
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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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Online adversarial purification based on self-supervision
Changhao Shi, Chester Holtz, and Gal Mishne · 2021
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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2021
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Robustifying models against adversarial attacks by langevin dynamics
Vignesh Srinivasan, Csaba Rohrer, Arturo Marban, Klaus-Robert Müller, Wojciech Samek, and Shinichi Nakajima · 2021
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Automated discovery of adaptive attacks on adversarial defenses
Chengyuan Yao, Pavol Bielik, Petar Tsankov, and Martin Vechev · 2021
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Applied stochastic differential equations
Simo Särkkä and Arno Solin · 2019
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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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Robustbench: a standardized adversarial robustness benchmark
Francesco Croce, Maksym Andriushchenko, Vikash Sehwag, Edoardo Debenedetti, Nicolas Flammarion, Mung Chiang, Prateek Mittal, and Matthias Hein · 2020
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Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
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An image is worth 16x16 words: Transformers for image recognition at scale
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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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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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Diffusion models for adversarial purification
Weili Nie, Brandon Guo, Yujia Huang, Chaowei Xiao, Arash Vahdat, and Anima Anandkumar · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Palette: Image-to-image diffusion models
Chitwan Saharia, William Chan, Huiwen Chang, Chris Lee, Jonathan Ho, Tim Salimans, David Fleet, and Mohammad Norouzi · 2022
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Photorealistic text-to-image diffusion models with deep language understanding
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, et al · 2022
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Image super-resolution via iterative refinement
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Pointdp: Diffusion-driven purification against adversarial attacks on 3d point cloud recognition
Jiachen Sun, Weili Nie, Zhiding Yu, Z Morley Mao, and Chaowei Xiao · 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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Guided diffusion model for adversarial purification from random noise
Quanlin Wu, Hang Ye, and Yuntian Gu · 2022
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Densepure: Understanding diffusion models towards adversarial robustness
Chaowei Xiao, Zhongzhu Chen, Kun Jin, Jiongxiao Wang, Weili Nie, Mingyan Liu, Anima Anandkumar, Bo Li, and Dawn Song · 2022
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Ada3diff: Defending against 3d adversarial point clouds via adaptive diffusion
Kui Zhang, Hang Zhou, Jie Zhang, Qidong Huang, Weiming Zhang, and Nenghai Yu · 2022
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Content-based unrestricted adversarial attack
Zhaoyu Chen, Bo Li, Shuang Wu, Kaixun Jiang, Shouhong Ding, and Wenqiang Zhang · 2023
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Decodingtrust: A comprehensive assessment of trustworthiness in gpt models
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Diffusion-based adversarial sample generation for improved stealthiness and controllability
Haotian Xue, Alexandre Araujo, Bin Hu, and Yongxin Chen · 2023
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