Nathan Inkawhich
Nathan Inkawhich
ECE Graduate Student, Duke University
Verified email at duke.edu
Title
Cited by
Cited by
Year
Feature space perturbations yield more transferable adversarial examples
N Inkawhich, W Wen, HH Li, Y Chen
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019
252019
Adversarial attacks for optical flow-based action recognition classifiers
N Inkawhich, M Inkawhich, Y Chen, H Li
arXiv preprint arXiv:1811.11875, 2018
52018
Transferable Perturbations of Deep Feature Distributions
N Inkawhich, KJ Liang, L Carin, Y Chen
Proceedings of the International Conference on Learning Representations …, 2020
42020
High-performance computing for automatic target recognition in synthetic aperture radar imagery
U Majumder, E Christiansen, Q Wu, N Inkawhich, E Blasch, J Nehrbass
Cyber Sensing 2017 10185, 1018508, 2017
32017
Advanced Techniques for Robust SAR ATR: Mitigating Noise and Phase Errors
N Inkawhich, E Davis, U Majumder, C Capraro, Y Chen
Proceedings of the International Radar Conference (RADAR), 2020, 2020
22020
Perturbing Across the Feature Hierarchy to Improve Standard and Strict Blackbox Attack Transferability
N Inkawhich, KJ Liang, B Wang, M Inkawhich, L Carin, Y Chen
arXiv preprint arXiv:2004.14861, 2020
12020
DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles
H Yang, J Zhang, H Dong, N Inkawhich, A Gardner, A Touchet, W Wilkes, ...
arXiv preprint arXiv:2009.14720, 2020
2020
Object Recognition With Raw, Approximate, and Fully Processed Synthetic Aperture Radar Data
N Inkawhich, Y Chen, U Majumder, Q Wu, C Capraro
Military Communications for the 21st Century (MILCOM 2018), 2018
2018
Wavelet decomposition to reduce clutter for SAR object classification using deep neural networks (Conference Presentation)
UK Majumder, N Inkawhich
Cyber Sensing 2018 10630, 106300P, 2018
2018
Supplemental Material: Feature Space Perturbations Yield More Transferable Adversarial Examples
N Inkawhich, W Wen, HH Li, Y Chen
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