| 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 | 6571 | 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 | 3367 | 2017 |
| Robustness of classifiers: from adversarial to random noise SM Moosavi-Dezfooli*, A Fawzi*, P Frossard Advances in neural information processing systems 29, 2016 | 440* | 2016 |
| 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 | 392 | 2019 |
| Empirical study of the topology and geometry of deep networks SM Moosavi-Dezfooli*, A Fawzi*, P Frossard, S Soatto IEEE Conference on Computer Vision and Pattern Recognition, 2018 | 246* | 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 | 239 | 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 | 238 | 2018 |
| The robustness of deep networks: A geometrical perspective A Fawzi, SM Moosavi-Dezfooli, P Frossard IEEE Signal Processing Magazine 34 (6), 50-62, 2017 | 232* | 2017 |
| Geometric robustness of deep networks: analysis and improvement C Kanbak, SM Moosavi-Dezfooli, P Frossard IEEE Conference on Computer Vision and Pattern Recognition, 2018 | 177 | 2018 |
| 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 | 147 | 2020 |
| 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 | 138 | 2019 |
| Reducing excessive margin to achieve a better accuracy vs. robustness trade-off R Rade, SM Moosavi-Dezfooli International Conference on Learning Representations, 2022 | 93 | 2022 |
| Robustness of classifiers to universal perturbations: A geometric perspective SM Moosavi-Dezfooli*, A Fawzi*, O Fawzi, P Frossard, S Soatto International Conference on Learning Representations, 2018 | 86* | 2018 |
| Helper-based adversarial training: Reducing excessive margin to achieve a better accuracy vs. robustness trade-off R Rade, SM Moosavi-Dezfooli ICML 2021 Workshop on Adversarial Machine Learning, 2021 | 76 | 2021 |
| Vehicle trajectory prediction works, but not everywhere M Bahari, S Saadatnejad, A Rahimi, M Shaverdikondori, AH Shahidzadeh, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 67 | 2022 |
| Optimism in the face of adversity: Understanding and improving deep learning through adversarial robustness G Ortiz-Jiménez, A Modas, SM Moosavi-Dezfooli, P Frossard Proceedings of the IEEE 109 (5), 635-659, 2021 | 65 | 2021 |
| Divide, denoise, and defend against adversarial attacks SM Moosavi-Dezfooli, A Shrivastava, O Tuzel arXiv preprint arXiv:1802.06806, 2018 | 63 | 2018 |
| A geometry-inspired decision-based attack Y Liu, SM Moosavi-Dezfooli, P Frossard Proceedings of the International Conference on Computer Vision, 2019 | 60 | 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 | 55 | 2020 |
| PRIME: A few primitives can boost robustness to common corruptions A Modas, R Rade, G Ortiz-Jiménez, SM Moosavi-Dezfooli, P Frossard ECCV 2022, 623-640, 2022 | 52 | 2022 |
Pascal FrossardEcole Polytechnique Fédérale de Lausanne, EPFLایمیل تأیید شده در epfl.ch