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Most adversarial attacks and defenses focus on perturbations within small $\ell_p$-norm constraints.
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, Geoffrey Hinton, et al · 2009
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Simple black-box adversarial perturbations for deep networks
Nina Narodytska and Shiva Prasad Kasiviswanathan · 2016
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Sergey Zagoruyko and Nikos Komodakis · 2016
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Decision-based adversarial attacks: Reliable attacks against black-box machine learning models
Wieland Brendel, Jonas Rauber, and Matthias Bethge · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 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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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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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
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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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Constructing unrestricted adversarial examples with generative models
Yang Song, Rui Shu, Nate Kushman, and Stefano Ermon · 2018
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
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Nesterov accelerated gradient and scale invariance for adversarial attacks
Jiadong Lin, Chuanbiao Song, Kun He, Liwei Wang, and John E Hopcroft · 2019
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Super-convergence: Very fast training of neural networks using large learning rates
Leslie N Smith and Nicholay Topin · 2019
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One pixel attack for fooling deep neural networks
Jiawei Su, Danilo Vasconcellos Vargas, and Kouichi Sakurai · 2019
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Improving transferability of adversarial examples with input diversity
Cihang Xie, Zhishuai Zhang, Yuyin Zhou, Song Bai, Jianyu Wang, Zhou Ren, and Alan L Yuille · 2019
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Theoretically principled trade-off between robustness and accuracy
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric Xing, Laurent El Ghaoui, and Michael Jordan · 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
Cited alongside, same era.
Stochastic security: Adversarial defense using long-run dynamics of energy-based models
Mitch Hill, Jonathan Mitchell, and Song-Chun Zhu · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
Adversarial purification with score-based generative models
Jongmin Yoon, Sung Ju Hwang, and Juho Lee · 2021
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Unsolved problems in ml safety, 2022
Dan Hendrycks, Nicholas Carlini, John Schulman, and Jacob Steinhardt · 2022
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3d common corruptions and data augmentation
Oğuzhan Fatih Kar, Teresa Yeo, Andrei Atanov, and Amir Zamir · 2022
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Elucidating the design space of diffusion-based generative models
Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine · 2022
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Refining generative process with discriminator guidance in score-based diffusion models
Dongjun Kim, Yeongmin Kim, Wanmo Kang, and Il-Chul Moon · 2022
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Diffusion models for adversarial purification
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On the generation of unrestricted adversarial examples
Mehrgan Khoshpasand and Ali Ghorbani · 2020
Cited alongside, same era.
Torchattacks: A pytorch repository for adversarial attacks
Hoki Kim · 2020
Cited alongside, same era.
Diffwave: A versatile diffusion model for audio synthesis
Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, and Bryan Catanzaro · 2020
Cited alongside, same era.
Perceptual adversarial robustness: Defense against unseen threat models
Cassidy Laidlaw, Sahil Singla, and Soheil Feizi · 2020
Cited alongside, same era.
Do adversarially robust imagenet models transfer better?
Hadi Salman, Andrew Ilyas, Logan Engstrom, Ashish Kapoor, and Aleksander Madry · 2020
Cited alongside, same era.
Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2020
Cited alongside, same era.
Fundamental tradeoffs between invariance and sensitivity to adversarial perturbations
Florian Tramèr, Jens Behrmann, Nicholas Carlini, Nicolas Papernot, and Jörn-Henrik Jacobsen · 2020
Cited alongside, same era.
Weili Nie, Brandon Guo, Yujia Huang, Chaowei Xiao, Arash Vahdat, and Anima Anandkumar · 2022
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Scalable diffusion models with transformers
William Peebles and Saining Xie · 2022
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The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2022
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Decoupled kullback-leibler divergence loss
Jiequan Cui, Zhuotao Tian, Zhisheng Zhong, Xiaojuan Qi, Bei Yu, and Hanwang Zhang · 2023
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Adversarial attack and defense for medical image analysis: Methods and applications
Junhao Dong, Junxi Chen, Xiaohua Xie, Jianhuang Lai, and Hao Chen · 2023
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Revisiting robustness in graph machine learning
Lukas Gosch, Daniel Sturm, Simon Geisler, and Stephan Günnemann · 2023
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Towards Compositional Adversarial Robustness: Generalizing Adversarial Training to Composite Semantic Perturbations
Lei Hsiung, Yun-Yun Tsai, Pin-Yu Chen, and Tsung-Yi Ho · 2023
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Diffattack: Evasion attacks against diffusion-based adversarial purification
Mintong Kang, Dawn Song, and Bo Li · 2023
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Predict, refine, synthesize: Self-guiding diffusion models for probabilistic time series forecasting
Marcel Kollovieh, Abdul Fatir Ansari, Michael Bohlke-Schneider, Jasper Zschiegner, Hao Wang, and Yuyang Wang · 2023
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Generative diffusion for 3d turbulent flows
Marten Lienen, Jan Hansen-Palmus, David Lüdke, and Stephan Günnemann · 2023
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Robust principles: Architectural design principles for adversarially robust cnns
ShengYun Peng, Weilin Xu, Cory Cornelius, Matthew Hull, Kevin Li, Rahul Duggal, Mansi Phute, Jason Martin, and Duen Horng Chau · 2023
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Better diffusion models further improve adversarial training
Zekai Wang, Tianyu Pang, Chao Du, Min Lin, Weiwei Liu, and Shuicheng Yan · 2023
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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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