| Perturbing across the feature hierarchy to improve standard and strict blackbox attack transferability N Inkawhich, K Liang, B Wang, M Inkawhich, L Carin, Y Chen Advances in Neural Information Processing Systems 33, 20791-20801, 2020 | 88 | 2020 |
| Bridging a gap in SAR-ATR: Training on fully synthetic and testing on measured data N Inkawhich, MJ Inkawhich, EK Davis, UK Majumder, E Tripp, C Capraro, ... IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2021 | 86 | 2021 |
| Adversarial attacks for optical flow-based action recognition classifiers N Inkawhich, M Inkawhich, Y Chen, H Li arXiv preprint arXiv:1811.11875, 2018 | 40 | 2018 |
| Training SAR-ATR models for reliable operation in open-world environments NA Inkawhich, EK Davis, MJ Inkawhich, UK Majumder, Y Chen IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2021 | 36 | 2021 |
| Snooping attacks on deep reinforcement learning M Inkawhich, Y Chen, H Li arXiv preprint arXiv:1905.11832, 2019 | 34 | 2019 |
| Tunable Hybrid Proposal Networks for the Open World M Inkawhich, N Inkawhich, H Li, Y Chen Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2024 | 4 | 2024 |
| Self-trained proposal networks for the open world M Inkawhich, N Inkawhich, H Li, Y Chen arXiv preprint arXiv:2208.11050, 2022 | 2 | 2022 |
| OSR-ViT: A Simple and Modular Framework for Open-Set Object Detection and Discovery M Inkawhich, N Inkawhich, H Yang, J Zhang, R Linderman, Y Chen arXiv preprint arXiv:2404.10865, 2024 | | 2024 |
| From Adversaries to Anomalies: Addressing Real-World Vulnerabilities of Deep Learning-Based Vision Models MJ Inkawhich Duke University, 2024 | | 2024 |
| The Untapped Potential of Off-the-Shelf Convolutional Neural Networks M Inkawhich, N Inkawhich, E Davis, H Li, Y Chen Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2022 | | 2022 |
