Follow
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
42352016
Universal adversarial perturbations
SM Moosavi-Dezfooli, A Fawzi, O Fawzi, P Frossard
Proceedings of the IEEE conference on computer vision and pattern …, 2017
22562017
Analysis of classifiers’ robustness to adversarial perturbations
A Fawzi, O Fawzi, P Frossard
Machine learning 107 (3), 481-508, 2018
390*2018
Robustness of classifiers: from adversarial to random noise
A Fawzi, SM Moosavi-Dezfooli, P Frossard
Advances in neural information processing systems 29, 2016
3442016
Analysis of classifiers' robustness to adversarial perturbations
A Fawzi, O Fawzi, P Frossard
arXiv preprint arXiv:1502.02590, 2015
3422015
Adversarial vulnerability for any classifier
A Fawzi, H Fawzi, O Fawzi
Advances in neural information processing systems 31, 2018
2342018
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
2332019
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
2232016
Adversarial robustness through local linearization
C Qin, J Martens, S Gowal, D Krishnan, K Dvijotham, A Fawzi, S De, ...
Advances in Neural Information Processing Systems 32, 2019
2172019
The robustness of deep networks: A geometrical perspective
A Fawzi, SM Moosavi-Dezfooli, P Frossard
IEEE Signal Processing Magazine 34 (6), 50-62, 2017
169*2017
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
166*2018
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
1292019
Are labels required for improving adversarial robustness?
JB Alayrac, J Uesato, PS Huang, A Fawzi, R Stanforth, P Kohli
Advances in Neural Information Processing Systems 32, 2019
1222019
Manitest: Are classifiers really invariant?
A Fawzi, P Frossard
arXiv preprint arXiv:1507.06535, 2015
1162015
Dictionary learning for fast classification based on soft-thresholding
A Fawzi, M Davies, P Frossard
International Journal of Computer Vision 114, 306-321, 2015
602015
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
512017
Discovering faster matrix multiplication algorithms with reinforcement learning
A Fawzi, M Balog, A Huang, T Hubert, B Romera-Paredes, M Barekatain, ...
Nature 610 (7930), 47-53, 2022
432022
Robustness of classifiers to uniform and Gaussian noise
JY Franceschi, A Fawzi, O Fawzi
International Conference on Artificial Intelligence and Statistics, 1280-1288, 2018
412018
Measuring the effect of nuisance variables on classifiers
A Fawzi, P Frossard
Proceedings of the British Machine Vision Conference (BMVC), 137.1-137.12, 2016
372016
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
332016
The system can't perform the operation now. Try again later.
Articles 1–20