| Robust physical-world attacks on deep learning visual classification K Eykholt, I Evtimov, E Fernandes, B Li, A Rahmati, C Xiao, A Prakash, ... Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 2391* | 2018 |
| Physical adversarial examples for object detectors D Song, K Eykholt, I Evtimov, E Fernandes, B Li, A Rahmati, F Tramer, ... 12th USENIX workshop on offensive technologies (WOOT 18), 2018 | 382 | 2018 |
| Internet of things security research: A rehash of old ideas or new intellectual challenges? E Fernandes, A Rahmati, K Eykholt, A Prakash IEEE Security & Privacy 15 (4), 79-84, 2017 | 114 | 2017 |
| Note on attacking object detectors with adversarial stickers K Eykholt, I Evtimov, E Fernandes, B Li, D Song, T Kohno, A Rahmati, ... arXiv preprint arXiv:1712.08062, 2017 | 40 | 2017 |
| Tyche: A risk-based permission model for smart homes A Rahmati, E Fernandes, K Eykholt, A Prakash 2018 IEEE Cybersecurity Development (SecDev), 29-36, 2018 | 29 | 2018 |
| Robust physical-world attacks on deep learning models (2017) K Eykholt, I Evtimov, E Fernandes, B Li, A Rahmati, C Xiao, A Prakash, ... arXiv preprint arXiv:1707.08945, 2018 | 16 | 2018 |
| Robust physical-world attacks on deep learning visual classification K Eykholt, I Evtimov, E Fernandes, B Li, A Rahmati, C Xiao, A Prakash, ... | 15 | 2020 |
| Tyche: Risk-based permissions for smart home platforms A Rahmati, E Fernandes, K Eykholt, A Prakash arXiv preprint arXiv:1801.04609, 2018 | 10 | 2018 |
| Can attention masks improve adversarial robustness? P Vaishnavi, T Cong, K Eykholt, A Prakash, A Rahmati Engineering Dependable and Secure Machine Learning Systems: Third …, 2020 | 8 | 2020 |
| Heimdall: A privacy-respecting implicit preference collection framework A Rahmati, E Fernandes, K Eykholt, X Chen, A Prakash Proceedings of the 15th Annual International Conference on Mobile Systems …, 2017 | 7 | 2017 |
| Transferring adversarial robustness through robust representation matching P Vaishnavi, K Eykholt, A Rahmati 31st USENIX Security Symposium (USENIX Security 22), 2083-2098, 2022 | 5 | 2022 |
| Separation of Powers in Federated Learning (Poster Paper) PC Cheng, K Eykholt, Z Gu, H Jamjoom, KR Jayaram, E Valdez, A Verma Proceedings of the First Workshop on Systems Challenges in Reliable and …, 2021 | 5 | 2021 |
| Ensuring authorized updates in multi-user database-backed applications K Eykholt, A Prakash, B Mozafari 26th {USENIX} Security Symposium ({USENIX} Security 17), 1445-1462, 2017 | 4 | 2017 |
| Designing adversarially resilient classifiers using resilient feature engineering K Eykholt, A Prakash arXiv preprint arXiv:1812.06626, 2018 | 3 | 2018 |
| Ares: A System-Oriented Wargame Framework for Adversarial ML F Ahmed, P Vaishnavi, K Eykholt, A Rahmati 2022 IEEE Security and Privacy Workshops (SPW), 73-79, 2022 | 2 | 2022 |
| Adaptive Verifiable Training Using Pairwise Class Similarity S Wang, K Eykholt, T Lee, J Jang, I Molloy Proceedings of the AAAI Conference on Artificial Intelligence 35 (11), 10201 …, 2021 | 2 | 2021 |
| Robust classification using robust feature augmentation K Eykholt, S Gupta, A Prakash, A Rahmati, P Vaishnavi, H Zheng arXiv preprint arXiv:1905.10904, 2019 | 2 | 2019 |
| Transferable Adversarial Robustness using Adversarially Trained Autoencoders. P Vaishnavi, K Eykholt, A Prakash, A Rahmati CoRR, 2019 | 2 | 2019 |
| Constraining neural networks for robustness through alternative encoding K Eykholt, T Lee, IM Molloy, J Jang US Patent App. 17/112,628, 2022 | 1 | 2022 |
| Designing and Evaluating Physical Adversarial Attacks and Defenses for Machine Learning Algorithms K Eykholt | 1 | 2019 |
