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Mode connectivity provides novel geometric insights on analyzing loss landscapes and enables building high-accuracy pathways between well-trained neural networks.
Fault injection attacks on cryptographic devices: Theory, practice, and countermeasures
A. Barenghi, L. Breveglieri, and et. al · 2012
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Poisoning attacks against support vector machines
Battista Biggio, Blaine Nelson, and Pavel Laskov · 2012
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The bernstein polynomial basis: A centennial retrospective
Rida T. Farouki · 2012
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Computer Graphics: Theory and Practice
Jonas Gomes, Luiz Velho, and Mario Costa Sousa · 2012
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Training deep and recurrent networks with hessian-free optimization
James Martens and Ilya Sutskever · 2012
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Towards query-efficient black-box adversary with zeroth-order natural gradient descent
Pu Zhao, Pin-Yu Chen, Siyue Wang, and Xue Lin · 2014
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Identity mappings in deep residual networks
Kaiminaripov He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Transferability in machine learning: from phenomena to black-box attacks using adversarial samples
Nicolas Papernot, Patrick McDaniel, and Ian Goodfellow · 2016
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Drammer: Deterministic rowhammer attacks on mobile platforms
Victor Van Der Veen, Yanick Fratantonio, and et. al · 2016
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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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The robustness of deep networks: A geometrical perspective
Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, and Pascal Frossard · 2017
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Pruning filters for efficient convnets
Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf · 2017
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Fault injection attack on deep neural network
Y. Liu, L. Wei, B. Luo, and Q. Xu · 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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Towards understanding generalization of deep learning: Perspective of loss landscapes
Lei Wu, Zhanxing Zhu, et al · 2017
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Wild patterns: Ten years after the rise of adversarial machine learning
Battista Biggio and Fabio Roli · 2018
Cited alongside, same era.
Detecting backdoor attacks on deep neural networks by activation clustering
Bryant Chen, Wilka Carvalho, Nathalie Baracaldo, Heiko Ludwig, Benjamin Edwards, Taesung Lee, Ian Molloy, and Biplav Srivastava · 2018
Cited alongside, same era.
Poison frogs! targeted clean-label poisoning attacks on neural networks
Ali Shafahi, W Ronny Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer, Tudor Dumitras, and Tom Goldstein · 2018
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Is robustness the cost of accuracy?–a comprehensive study on the robustness of 18 deep image classification models
Dong Su, Huan Zhang, Hongge Chen, Jinfeng Yi, Pin-Yu Chen, and Yupeng Gao · 2018
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Spectral signatures in backdoor attacks
Brandon Tran, Jerry Li, and Aleksander Madry · 2018
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Identifying generalization properties in neural networks
Huan Wang, Nitish Shirish Keskar, Caiming Xiong, and Richard Socher · 2018
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Defending dnn adversarial attacks with pruning and logits augmentation
S. Wang, X. Wang, S. Ye, P. Zhao, and X. Lin · 2018
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Elvis Dohmatob · 2018
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Essentially no barriers in neural network energy landscape
Felix Draxler, Kambis Veschgini, Manfred Salmhofer, and Fred Hamprecht · 2018
Cited alongside, same era.
Empirical study of the topology and geometry of deep networks
Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard, and Stefano Soatto · 2018
Cited alongside, same era.
Loss surfaces, mode connectivity, and fast ensembling of DNNs
Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry P Vetrov, and Andrew G Wilson · 2018
Cited alongside, same era.
Using mode connectivity for loss landscape analysis
Akhilesh Gotmare, Nitish Shirish Keskar, Caiming Xiong, and Richard Socher · 2018
Cited alongside, same era.
Black-box adversarial attacks with limited queries and information
Andrew Ilyas, Logan Engstrom, Anish Athalye, and Jessy Lin · 2018
Cited alongside, same era.
Manipulating machine learning: Poisoning attacks and countermeasures for regression learning
Matthew Jagielski, Alina Oprea, Battista Biggio, Chang Liu, Cristina Nita-Rotaru, and Bo Li · 2018
Cited alongside, same era.
Structured Adversarial Attack: Towards General Implementation and Better Interpretability
K. Xu, S. Liu, P. Zhao, P.-Y. Chen, H. Zhang, D. Erdogmus, Y. Wang, and X. Lin · 2018
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Interpreting adversarial robustness: A view from decision surface in input space
Fuxun Yu, Chenchen Liu, Yanzhi Wang, and Xiang Chen · 2018
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An admm-based universal framework for adversarial attacks on deep neural networks
Pu Zhao, Sijia Liu, Yanzhi Wang, and Xue Lin · 2018
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Adversarial examples from computational constraints
Sébastien Bubeck, Eric Price, and Ilya Razenshteyn · 2019
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BadNets: Evaluating backdooring attacks on deep neural networks
T. Gu, K. Liu, B. Dolan-Gavitt, and S. Garg · 2019
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Robustness via curvature regularization, and vice versa
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Jonathan Uesato, and Pascal Frossard · 2019
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Robustness may be at odds with accuracy
Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Madry · 2019
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Neural cleanse: Identifying and mitigating backdoor attacks in neural networks
Bolun Wang, Yuanshun Yao, Shawn Shan, Huiying Li, Bimal Viswanath, Haitao Zheng, and Ben Y Zhao · 2019
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On the design of black-box adversarial examples by leveraging gradient-free optimization and operator splitting method
Pu Zhao, Sijia Liu, Pin-Yu Chen, Nghia Hoang, Kaidi Xu, Bhavya Kailkhura, and Xue Lin · 2019
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