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ORCIDChris M. Ward
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- Chris Ward
- Chris C. Ward
- Chris L. Ward
- Chris R. Ward
- T. J. Christopher Ward (aka: T. J. Chris Ward) — IBM Research, UK
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2020 – today
- 2024
[i4]Chris M. Ward, Josh Harguess, Julia Tao, Daniel Christman, Paul Spicer, Mike Tan:
The AI Security Pyramid of Pain. CoRR abs/2402.11082 (2024)- 2022
- [c8]Mohammad R. Alam, Chris M. Ward:
Adversarial Examples in Self-Driving: A Review of Available Datasets and Attacks. AIPR 2022: 1-6 - [c7]Josh Harguess, Chris M. Ward:
Is the Next Winter Coming for AI? Elements of Making Secure and Robust AI. AIPR 2022: 1-7 - [c6]Melanie Jutras, Ethan Liang, Sara Leary, Chris M. Ward, Keith Manville:
Detecting Physical Adversarial Patch Attacks with Object Detectors. AIPR 2022: 1-7 - [c5]Mohammad Zarei, Chris M. Ward, Josh Harguess, Marshal Aiken:
Adversarial Barrel! An Evaluation of 3D Physical Adversarial Attacks. AIPR 2022: 1-6 - 2021
- [c4]Emily Holt
, Anto Malkastian, Savanna Smith, Chris M. Ward, Josh Harguess:
Baseline Evaluation Methodology for Adversarial Patterns on Object Detection Models. AIPR 2021: 1-6 - [c3]Michael Threet, Colin Busho, Josh Harguess, Melanie Jutras, Nicole Lape, Sara Leary, Keith Manville, Mike Tan, Chris M. Ward:
Physical Adversarial Attacks in Simulated Environments. AIPR 2021: 1-5
2010 – 2019
- 2019
- [c2]Jonathan Sato, Chelsea Mediavilla, Chris M. Ward, Shibin Parameswaran:
SRC3: A Video Dataset for Evaluating Domain Mismatch. AIPR 2019: 1-7 - [i3]Chris M. Ward, Josh Harguess, Cameron Hilton:
Ship classification from overhead imagery using synthetic data and domain adaptation. CoRR abs/1905.03894 (2019) - [i2]Chris M. Ward, Josh Harguess, Alexander G. Corelli:
Leveraging synthetic imagery for collision-at-sea avoidance. CoRR abs/1905.04828 (2019) - [i1]Chris M. Ward, Josh Harguess, Brendan Crabb, Shibin Parameswaran:
Image quality assessment for determining efficacy and limitations of Super-Resolution Convolutional Neural Network (SRCNN). CoRR abs/1905.05373 (2019) - 2018
- [c1]Marissa Dotter, Chris M. Ward:
Visualizing Compression of Deep Learning Models for Classification. AIPR 2018: 1-8
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