Automated detection of pitting and stress corrosion cracks in used nuclear fuel dry storage canisters using residual neural networks. - Theodore Papamarkou; Hayley Guy; Bryce Kroencke; Jordan Miller; Preston Robinette; Daniel Schultz; Jacob D. Hinkle; Laura Pullum; Catherine D. Schuman; Jeremy Renshaw; Stylianos Chatzidakis (CoRR, 2020)

On Using Real-Time Reachability for the Safety Assurance of Machine Learning Controllers. - Patrick Musau; Nathaniel Hamilton; Diego Manzanas Lopez; Preston Robinette; Taylor T. Johnson (ICAA, 2022)

Training Agents to Satisfy Timed and Untimed Signal Temporal Logic Specifications with Reinforcement Learning. - Nathaniel Hamilton; Preston Robinette; Taylor T. Johnson (SEFM, 2022)

An Empirical Analysis of the Use of Real-Time Reachability for the Safety Assurance of Autonomous Vehicles. - Patrick Musau; Nathaniel Hamilton; Diego Manzanas Lopez; Preston Robinette; Taylor T. Johnson (CoRR, 2022)

On Using Real-Time Reachability for the Safety Assurance of Machine Learning Controllers. - Patrick Musau; Nathaniel Hamilton; Diego Manzanas Lopez; Preston Robinette; Taylor T. Johnson (Zenodo, 2022)

An Empirical Analysis of the Use of Real-Time Reachability for the Safety Assurance of Autonomous Vehicles. - Patrick Musau; Nathaniel Hamilton; Diego Manzanas Lopez; Preston Robinette; Taylor T. Johnson (Zenodo, 2022)

SUDS: Sanitizing Universal and Dependent Steganography. - Preston K. Robinette; Hanchen D. Wang; Nishan Shehadeh; Daniel Moyer; Taylor T. Johnson (ECAI, 2023)

Self-Preserving Genetic Algorithms for Safe Learning in Discrete Action Spaces. - Preston K. Robinette; Nathaniel P. Hamilton; Taylor T. Johnson (ICCPS, 2023)

DEMO: Self-Preserving Genetic Algorithms vs. Safe Reinforcement Learning in Discrete Action Spaces. - Preston K. Robinette; Nathaniel P. Hamilton; Taylor T. Johnson (ICCPS, 2023)

Benchmark: Neural Network Malware Classification. - Preston K. Robinette; Diego Manzanas Lopez; Taylor T. Johnson (AISoLA, 2023)

Monsters in the Dark: Sanitizing Hidden Threats with Diffusion Models. - Preston K. Robinette; Daniel Moyer; Taylor T. Johnson (CoRR, 2023)

(Artifact) Case Study: Neural Network Malware Detection Verification for Feature and Image Datasets. - Preston Robinette; Diego Manzanas Lopez; Taylor T. Johnson (Zenodo, 2023)

Sanitizing Hidden Information with Diffusion Models. - Preston K. Robinette; Daniel Moyer; Taylor T. Johnson (ECAI, 2024)

FairNNV: The Neural Network Verification Tool For Certifying Fairness. - Anne M. Tumlin; Diego Manzanas Lopez; Preston Robinette; Yuying Zhao; Tyler Derr; Taylor T. Johnson (ICAIF, 2024)

Case Study: Neural Network Malware Detection Verification for Feature and Image Datasets. - Preston K. Robinette; Diego Manzanas Lopez; Serena Serbinowska; Kevin Leach; Taylor T. Johnson (FormaliSE@ICSE, 2024)

Trigger-Based Fragile Model Watermarking for Image Transformation Networks. - Preston K. Robinette; Dung Thuy Nguyen; Samuel Sasaki; Taylor T. Johnson (CoRR, 2024)

Formalizing Stateful Behavior Trees. - Serena Serafina Serbinowska; Preston Robinette; Gabor Karsai; Taylor T. Johnson (FMAS@iFM, 2024)

EditScout: Locating Forged Regions from Diffusion-based Edited Images with Multimodal LLM. - Quang Nguyen; Truong Vu; Trong-Tung Nguyen; Yuxin Wen; Preston K. Robinette; Taylor T. Johnson; Tom Goldstein; Anh Tran 001; Khoi Nguyen 001 (CoRR, 2024)

2024 SEFM Artifact for BehaVerify (Formalizing Stateful Behavior Trees). - Serena Serafina Serbinowska; Preston Robinette; Gabor Karsai; Taylor Thomas Johnson (Zenodo, 2024)

Robustness Verification of Video Classification Neural Networks. - Samuel Sasaki; Diego Manzanas Lopez; Preston K. Robinette; Taylor T. Johnson (FormaliSE, 2025)

Blind Visible Watermark Removal with Morphological Dilation. - Preston K. Robinette; Taylor T. Johnson (CoRR, 2025)