| 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 | 2102 | 2016 |
| Universal adversarial perturbations SM Moosavi-Dezfooli, A Fawzi, O Fawzi, P Frossard Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017 | 1120 | 2017 |
| Robustness of classifiers: from adversarial to random noise A Fawzi, SM Moosavi-Dezfooli, P Frossard arXiv preprint arXiv:1608.08967, 2016 | 230 | 2016 |
| The robustness of deep networks: A geometrical perspective A Fawzi, SM Moosavi-Dezfooli, P Frossard IEEE Signal Processing Magazine 34 (6), 50-62, 2017 | 111* | 2017 |
| Robustness of Classifiers to Universal Perturbations: A Geometric Perspective SM Moosavi-Dezfooli, A Fawzi, O Fawzi, P Frossard, S Soatto Sixth International Conference on Learning Representations, 2018 | 95* | 2018 |
| Empirical study of the topology and geometry of deep networks A Fawzi, SM Moosavi-Dezfooli, P Frossard, S Soatto IEEE Conference on Computer Vision and Pattern Recognition, 2018 | 90* | 2018 |
| Robustness via curvature regularization, and vice versa SM Moosavi-Dezfooli, A Fawzi, J Uesato, P Frossard IEEE Conference on Computer Vision and Pattern Recognition, 2019 | 84 | 2019 |
| Adaptive quantization for deep neural network Y Zhou, SM Moosavi-Dezfooli, NM Cheung, P Frossard Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 55 | 2018 |
| Geometric robustness of deep networks: analysis and improvement C Kanbak, SM Moosavi-Dezfooli, P Frossard IEEE Conference on Computer Vision and Pattern Recognition, 2018 | 54 | 2018 |
| SparseFool: a few pixels make a big difference A Modas, SM Moosavi-Dezfooli, P Frossard IEEE Conference on Computer Vision and Pattern Recognition, 2019 | 50 | 2019 |
| Universal Adversarial Attacks on Text Classifiers M Behjati, SM Moosavi-Dezfooli, M Soleymani Baghshah, P Frossard International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2019 | 25 | 2019 |
| Divide, denoise, and defend against adversarial attacks SM Moosavi-Dezfooli, A Shrivastava, O Tuzel arXiv preprint arXiv:1802.06806, 2018 | 18 | 2018 |
| A geometry-inspired decision-based attack Y Liu, SM Moosavi-Dezfooli, P Frossard Proceedings of the International Conference on Computer Vision, 2019 | 9 | 2019 |
| Hold me tight! Influence of discriminative features on deep network boundaries G Ortiz-Jimenez, A Modas, SM Moosavi-Dezfooli, P Frossard Advances in Neural Information Processing Systems, 2020 | 5 | 2020 |
| GeoDA: a geometric framework for black-box adversarial attacks A Rahmati, SM Moosavi-Dezfooli, P Frossard, H Dai Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2020 | 3 | 2020 |
| Simultaneous acoustic localization of multiple smartphones with euclidean distance matrices SM Moosavi-Dezfooli, YA Pignolet, D Dzung International Conference on Embedded Wireless Systems and Networks, 2016 | 2 | 2016 |
| Optimism in the face of adversity: Understanding and improving deep learning through adversarial robustness G Ortiz-Jiménez, A Modas, SM Moosavi-Dezfooli, P Frossard arXiv preprint arXiv:2010.09624, 2020 | 1 | 2020 |
| Redundant features can hurt robustness to distributions shift G Ortiz Jimenez, A Modas, SM Moosavi Dezfooli, P Frossard Uncertainty & Robustness in Deep Learning Workshop (ICML 2020), 2020 | 1 | 2020 |
| Neural Anisotropy Directions G Ortiz-Jimenez, A Modas, SM Moosavi-Dezfooli, P Frossard Advances in Neural Information Processing Systems, 2020 | 1 | 2020 |
| Geometry of adversarial robustness of deep networks: methods and applications SM Moosavi Dezfooli EPFL, 2019 | 1* | 2019 |
Pascal FrossardEcole Polytechnique Fédérale de Lausanne, EPFLVerified email at epfl.ch
