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Pages
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Posts
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portfolio
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publications
Overcoming Adversarial Attacks for Hunam-in-the-Loop Applications
Published in ICML 2022, New Frontiers in Adversarial Machine Learning Workshop, 2022
Including human analysis has the potential to positively affect the robustness of Deep Neural Networks and is relatively unexplored in the Adversarial Machine Learning literature. Neural network visual explanation maps have been shown to be prone to adversarial attacks. Further research is needed in order to select robust visualizations of explanations for the image analyst to evaluate a given model. These factors greatly impact Human-In-The-Loop (HITL) evaluation tools due to their reliance on adversarial images, including explanation maps and measurements of robustness. We believe models of human visual attention may improve interpretability and robustness of human-machine imagery analysis systems. Our challenge remains, how can HITL evaluation be robust in this adversarial landscape?
Recommended citation: McCoppin, R., Kennedy, M., Lukyanenko, P., & Kennedy, SM. (2022). Overcoming Adversarial Attacks for Hunam-in-the-Loop Applications. 39th International Conference on Machine Learning, New Frontiers in Adversarial Machine Learning Workshop. Baltimore, Maryland. https://arxiv.org/abs/2306.05952
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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