| Deceiving google's perspective api built for detecting toxic comments H Hosseini, S Kannan, B Zhang, R Poovendran arXiv preprint arXiv:1702.08138, 2017 | 441 | 2017 |
| Semantic adversarial examples H Hosseini, R Poovendran Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 255 | 2018 |
| On the Limitation of Convolutional Neural Networks in Recognizing Negative Images H Hosseini, B Xiao, M Jaiswal, R Poovendran Machine Learning and Applications (ICMLA), 2017 16th IEEE International …, 2017 | 208 | 2017 |
| Google's cloud vision api is not robust to noise H Hosseini, B Xiao, R Poovendran 2017 16th IEEE international conference on machine learning and applications …, 2017 | 178 | 2017 |
| Blocking transferability of adversarial examples in black-box learning systems H Hosseini, Y Chen, S Kannan, B Zhang, R Poovendran arXiv preprint arXiv:1703.04318, 2017 | 165 | 2017 |
| Real-time impulse noise suppression from images using an efficient weighted-average filtering H Hosseini, F Hessar, F Marvasti IEEE Signal Processing Letters 22 (8), 1050-1054, 2014 | 78 | 2014 |
| Fast restoration of natural images corrupted by high-density impulse noise H Hosseini, F Marvasti EURASIP Journal on Image and Video Processing 2013 (1), 15, 2013 | 60 | 2013 |
| Attacking Automatic Video Analysis Algorithms: A Case Study of Google Cloud Video Intelligence API H Hosseini, B Xiao, A Clark, R Poovendran Proceedings of the 2017 on Multimedia Privacy and Security, 21-32, 2017 | 46 | 2017 |
| Assessing shape bias property of convolutional neural networks H Hosseini, B Xiao, M Jaiswal, R Poovendran Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 41 | 2018 |
| Federated Learning of User Verification Models Without Sharing Embeddings H Hosseini, H Park, S Yun, C Louizos, J Soriaga, M Welling ICML '21, 2021 | 36 | 2021 |
| Federated learning of user authentication models H Hosseini, S Yun, H Park, C Louizos, J Soriaga, M Welling arXiv preprint arXiv:2007.04618, 2020 | 29 | 2020 |
| Are odds really odd? bypassing statistical detection of adversarial examples H Hosseini, S Kannan, R Poovendran arXiv preprint arXiv:1907.12138, 2019 | 29 | 2019 |
| Battery degradation temporal modeling using lstm networks M Assefi, A Hooshmand, H Hosseini, R Sharma 2018 17th IEEE international conference on machine learning and applications …, 2018 | 23* | 2018 |
| Deceiving google’s cloud video intelligence api built for summarizing videos H Hosseini, B Xiao, R Poovendran CVPR Workshop, 2017 | 22* | 2017 |
| Despicable me (ter): Anonymous and fine-grained metering data reporting with dishonest meters M Ambrosin, H Hosseini, K Mandal, M Conti, R Poovendran 2016 IEEE conference on communications and network security (CNS), 163-171, 2016 | 22 | 2016 |
| Unsupervised information obfuscation for split inference of neural networks M Samragh, H Hosseini, A Triastcyn, K Azarian, J Soriaga, F Koushanfar arXiv preprint arXiv:2104.11413, 2021 | 18 | 2021 |
| Dropping pixels for adversarial robustness H Hosseini, S Kannan, R Poovendran Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 15 | 2019 |
| Verifiable and privacy-preserving fine-grained data-collection for smart metering M Ambrosin, H Hosseini, K Mandal, M Conti, R Poovendran 2015 IEEE Conference on Communications and Network Security (CNS), 655-658, 2015 | 14 | 2015 |
| Modeling and Mitigating the Coremelt Attack G Yang, H Hosseini, D Sahabandu, A Clark, J Hespanha, R Poovendran ACC 2018, 2018 | 12 | 2018 |
| A game theoretical framework for inter-process adversarial intervention detection MO Sayin, H Hosseini, R Poovendran, T Başar International Conference on Decision and Game Theory for Security, 486-507, 2018 | 9 | 2018 |
Radha PoovendranProfessor of ECE, University of WashingtonVerified email at uw.edu
