Published in ICLR 24, 2024
Revamp: Automated Simulations of Adversarial Attacks on Arbitrary Objects in Realistic Scenes
Recommended citation: Hull, Matthew, et. al. (2024). "Revamp: Automated Simulations of Adversarial Attacks on Arbitrary Objects in Realistic Scenes." ICLR 24. https://matthewdhull.github.io/files/revamp.pdf
Published in ICLR 24, 2024
LLM Self Defense
Recommended citation: Phute, Mansi, et. al. (2024). "LLM Self Defense: By Self Examination, LLMs Know They Are Being Tricked." ICLR 24. https://arxiv.org/pdf/2308.07308
Published in VIS 23, 2023
Automatic grading of D3 visualizations.
Recommended citation: Hull, Matthew, et. al. (2023). "VisGrader: Automatic Grading of D3 Visualizations." VIS 23. https://arxiv.org/pdf/2310.12347.pdf
Published in BMVC 23, 2023
A suite of generalizable robust architectural design principles.
Recommended citation: Peng, Anthony, et. al. (2023). "Robust Principles: Architectural Design Principles for Adversarially Robust CNNs." BMVC 23. https://arxiv.org/pdf/2308.16258.pdf
Published in CVPR 22, 2022
Interactive visual tool that aims to help users better understand the behaviors of a model as adversarial images journey through an object detector.
Recommended citation: Sivapriya, Vellaichamy, et. al. (2022). "DetectorDetective: Investigating the Effects of Adversarial Examples on Object Detectors." CVPR 22. https://matthewdhull.github.io/files/detector_detective.pdf
Published in IEEE VIS 21, 2021
First-of-its-kind automatic grading approach for D3 visualizations that scalably and precisely evaluates data bindings, visual encodings, interactions, and design specifications used in a visualization.
Recommended citation: Hull, Matthew, et. al. (2021). "Towards Automatic Grading of D3.js Visualizations." IEEE VIS 21. https://matthewdhull.github.io/files/autograde_viz.pdf
Published in CIKM 20, 2020
In-browser interactive graph exploration and visualization tool.
Recommended citation: Li, Siwei, et. al. (2020). "Argo Lite: Open-Source Interactive Graph Exploration and Visualization in Browsers." CIKM 20. https://matthewdhull.github.io/files/argolite.pdf