Alhussein Fawzi
Alhussein Fawzi
Research Scientist, Google DeepMind
Verified email at google.com - Homepage
Title
Cited by
Cited by
Year
Deepfool: a simple and accurate method to fool deep neural networks
SM Moosavi-Dezfooli, A Fawzi, P Frossard
Proceedings of the IEEE conference on computer vision and pattern …, 2016
26342016
Universal adversarial perturbations
SM Moosavi-Dezfooli, A Fawzi, O Fawzi, P Frossard
Proceedings of the IEEE conference on computer vision and pattern …, 2017
13592017
Analysis of classifiers’ robustness to adversarial perturbations
A Fawzi, O Fawzi, P Frossard
Machine Learning 107 (3), 481-508, 2018
314*2018
Robustness of classifiers: from adversarial to random noise
A Fawzi, SM Moosavi-Dezfooli, P Frossard
arXiv preprint arXiv:1608.08967, 2016
2902016
Analysis of classifiers' robustness to adversarial perturbations
A Fawzi, O Fawzi, P Frossard
arXiv preprint arXiv:1502.02590, 2015
2742015
Adversarial vulnerability for any classifier
A Fawzi, H Fawzi, O Fawzi
arXiv preprint arXiv:1802.08686, 2018
1532018
Adaptive data augmentation for image classification
A Fawzi, H Samulowitz, D Turaga, P Frossard
2016 IEEE international conference on image processing (ICIP), 3688-3692, 2016
1482016
Robustness via curvature regularization, and vice versa
SM Moosavi-Dezfooli, A Fawzi, J Uesato, P Frossard
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019
1252019
The robustness of deep networks: A geometrical perspective
A Fawzi, SM Moosavi-Dezfooli, P Frossard
IEEE Signal Processing Magazine 34 (6), 50-62, 2017
122*2017
Adversarial robustness through local linearization
C Qin, J Martens, S Gowal, D Krishnan, K Dvijotham, A Fawzi, S De, ...
arXiv preprint arXiv:1907.02610, 2019
1162019
Empirical study of the topology and geometry of deep networks
A Fawzi, SM Moosavi-Dezfooli, P Frossard, S Soatto
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018
109*2018
Analysis of universal adversarial perturbations
SM Moosavi-Dezfooli, A Fawzi, O Fawzi, P Frossard, S Soatto
ArXiv e-prints, arXiv: 1705.09554, 2017
882017
Manitest: Are classifiers really invariant?
A Fawzi, P Frossard
arXiv preprint arXiv:1507.06535, 2015
862015
Are Labels Required for Improving Adversarial Robustness?
J Uesato, JB Alayrac, PS Huang, R Stanforth, A Fawzi, P Kohli
arXiv preprint arXiv:1905.13725, 2019
802019
Dictionary learning for fast classification based on soft-thresholding
A Fawzi, M Davies, P Frossard
International Journal of Computer Vision 114 (2), 306-321, 2015
542015
Are labels required for improving adversarial robustness?
JB Alayrac, J Uesato, PS Huang, A Fawzi, R Stanforth, P Kohli
472019
Robustness of classifiers to universal perturbations: A geometric perspective
SM Moosavi-Dezfooli, A Fawzi, O Fawzi, P Frossard, S Soatto
arXiv preprint arXiv:1705.09554, 2017
302017
Measuring the effect of nuisance variables on classifiers
A Fawzi, P Frossard
British Machine Vision Conference (BMVC), 2016
262016
Image inpainting through neural networks hallucinations
A Fawzi, H Samulowitz, D Turaga, P Frossard
2016 IEEE 12th Image, Video, and Multidimensional Signal Processing Workshop …, 2016
252016
Robustness of classifiers to uniform and Gaussian noise
JY Franceschi, A Fawzi, O Fawzi
International Conference on Artificial Intelligence and Statistics, 1280-1288, 2018
212018
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