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The lack of adversarial robustness has been recognized as an important issue for state-of-the-art machine learning (ML) models, e.g., deep neural networks (DNNs).
You only propagate once: Accelerating adversarial training via maximal principle
Dinghuai Zhang, Tianyuan Zhang, Yiping Lu, Zhanxing Zhu, and Bin Dong · 1905
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Stochastic first-and zeroth-order methods for nonconvex stochastic programming
S. Ghadimi and G. Lan · 2013
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Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
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Explaining and harnessing adversarial examples
Ian Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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A comprehensive linear speedup analysis for asynchronous stochastic parallel optimization from zeroth-order to first-order
X. Lian, H. Zhang, C.-J. Hsieh, Y. Huang, and J. Liu · 2016
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Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections
Xiaojiao Mao, Chunhua Shen, and Yu-Bin Yang · 2016
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The limitations of deep learning in adversarial settings
Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z Berkay Celik, and Ananthram Swami · 2016
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Tom B Brown, Dandelion Mané, Aurko Roy, Martín Abadi, and Justin Gilmer · 2017
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
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Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models
Pin-Yu Chen, Huan Zhang, Yash Sharma, Jinfeng Yi, and Cho-Jui Hsieh · 2017
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Maximum resilience of artificial neural networks
Chih-Hong Cheng, Georg Nührenberg, and Harald Ruess · 2017
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Output range analysis for deep neural networks
Souradeep Dutta, Susmit Jha, Sriram Sanakaranarayanan, and Ashish Tiwari · 2017
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Formal verification of piece-wise linear feed-forward neural networks
Ruediger Ehlers · 2017
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Robust physical-world attacks on deep learning models
Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Chaowei Xiao, Atul Prakash, Tadayoshi Kohno, and Dawn Song · 2017
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Adversarial and clean data are not twins
Zhitao Gong, Wenlu Wang, and Wei-Shinn Ku · 2017
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On the (statistical) detection of adversarial examples
Kathrin Grosse, Praveen Manoharan, Nicolas Papernot, Michael Backes, and Patrick McDaniel · 2017
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Countering adversarial images using input transformations
Chuan Guo, Mayank Rana, Moustapha Cissé, and Laurens van der Maaten · 2017
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Reluplex: An efficient smt solver for verifying deep neural networks
Guy Katz, Clark Barrett, David L Dill, Kyle Julian, and Mykel J Kochenderfer · 2017
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Magnet: a two-pronged defense against adversarial examples
Dongyu Meng and Hao Chen · 2017
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On detecting adversarial perturbations
Jan Hendrik Metzen, Tim Genewein, Volker Fischer, and Bastian Bischoff · 2017
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Practical black-box attacks against machine learning
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Berkay Celik, and Ananthram Swami · 2017
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Provable defenses against adversarial examples via the convex outer adversarial polytope
Eric Wong and J Zico Kolter · 2017
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Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising
Kai Zhang, Wangmeng Zuo, Yunjin Chen, Deyu Meng, and Lei Zhang · 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
Cited alongside, same era.
A unified view of piecewise linear neural network verification
Rudy R Bunel, Ilker Turkaslan, Philip Torr, Pushmeet Kohli, and Pawan K Mudigonda · 2018
Cited alongside, same era.
A dual approach to scalable verification of deep networks
Krishnamurthy Dvijotham, Robert Stanforth, Sven Gowal, Timothy Mann, and Pushmeet Kohli · 2018
Cited alongside, same era.
On the information-adaptive variants of the admm: an iteration complexity perspective
Xiang Gao, Bo Jiang, and Shuzhong Zhang · 2018
Cited alongside, same era.
Zeroth-order stochastic variance reduction for nonconvex optimization
S. Liu, B. Kailkhura, P.-Y. Chen, P. Ting, S. Chang, and L. Amini · 2018
Cited alongside, same era.
On instabilities of deep learning in image reconstruction and the potential costs of ai
Vegard Antun, Francesco Renna, Clarice Poon, Ben Adcock, and Anders C Hansen · 2020
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Zeroth-order regularized optimization (zoro): Approximately sparse gradients and adaptive sampling
HanQin Cai, Daniel Mckenzie, Wotao Yin, and Zhenliang Zhang · 2020
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Adversarial robustness: From self-supervised pretraining to fine-tuning
T. Chen, S. Liu, S. Chang, Y. Cheng, L. Amini, and Z. Wang · 2020
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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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Black-box adversarial attack with transferable model-based embedding
Zhichao Huang and Tong Zhang · 2020
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
Cited alongside, same era.
Certified defenses against adversarial examples
Aditi Raghunathan, Jacob Steinhardt, and Percy Liang · 2018
Cited alongside, same era.
Scaling provable adversarial defenses
Eric Wong, Frank Schmidt, Jan Hendrik Metzen, and J Zico Kolter · 2018
Cited alongside, same era.
Unlabeled data improves adversarial robustness
Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, Percy Liang, and John C Duchi · 2019
Cited alongside, same era.
Certified adversarial robustness via randomized smoothing
Jeremy M Cohen, Elan Rosenfeld, and J Zico Kolter · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Model inversion networks for model-based optimization
Aviral Kumar and Sergey Levine · 2019
Cited alongside, same era.
Adnan Qayyum, Junaid Qadir, Muhammad Bilal, and Ala Al-Fuqaha · 2020
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Improving robustness of deep-learning-based image reconstruction
Ankit Raj, Yoram Bresler, and Bo Li · 2020
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Denoised smoothing: A provable defense for pretrained classifiers
Hadi Salman, Mingjie Sun, Greg Yang, Ashish Kapoor, and J Zico Kolter · 2020
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The enigma-epilepsy working group: Mapping disease from large data sets
Sanjay M Sisodiya, Christopher D Whelan, Sean N Hatton, Khoa Huynh, Andre Altmann, Mina Ryten, Annamaria Vezzani, Maria Eugenia Caligiuri, Angelo Labate, and Antonio Gambardella · 2020
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Towards robust lidar-based perception in autonomous driving: General black-box adversarial sensor attack and countermeasures
Jiachen Sun, Yulong Cao, Qi Alfred Chen, and Z Morley Mao · 2020
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On adaptive attacks to adversarial example defenses
Florian Tramer, Nicholas Carlini, Wieland Brendel, 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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Adversarial T-Shirt! evading person detectors in a physical world
Kaidi Xu, Gaoyuan Zhang, S. Liu, Quanfu Fan, Mengshu Sun, Hongge Chen, Pin-Yu Chen, Yanzhi Wang, and Xue Lin · 2020
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Boosting adversarial robustness using feature level stochastic smoothing
Sravanti Addepalli, Samyak Jain, Gaurang Sriramanan, and R Venkatesh Babu · 2021
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Adversarial example detection for dnn models: A review
Ahmed Aldahdooh, Wassim Hamidouche, Sid Ahmed Fezza, and Olivier Deforges · 2021
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A zeroth-order block coordinate descent algorithm for huge-scale black-box optimization
HanQin Cai, Yuchen Lou, Daniel McKenzie, and Wotao Yin · 2021
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Parallel rectangle flip attack: A query-based black-box attack against object detection
Siyuan Liang, Baoyuan Wu, Yanbo Fan, Xingxing Wei, and Xiaochun Cao · 2021
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Certified patch robustness via smoothed vision transformers
Hadi Salman, Saachi Jain, Eric Wong, and Aleksander Mądry · 2021
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Self adversarial attack as an augmentation method for immunohistochemical stainings
Jelica Vasiljević, Friedrich Feuerhake, Cédric Wemmert, and Thomas Lampert · 2021
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Di-aa: An interpretable white-box attack for fooling deep neural networks
Yixiang Wang, Jiqiang Liu, Xiaolin Chang, Jianhua Wang, and Ricardo J Rodríguez · 2021
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Meta gradient adversarial attack
Zheng Yuan, Jie Zhang, Yunpei Jia, Chuanqi Tan, Tao Xue, and Shiguang Shan · 2021
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Defense against adversarial attacks by reconstructing images
Shudong Zhang, Haichang Gao, and Qingxun Rao · 2021
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