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Search Publications by: Amy N. Yates (Fed)

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Displaying 1 - 25 of 44

Who Is That? Perceptual Expertise on Other-Race Face Comparisons, Disguised Face Comparisons, and Face Memory

April 20, 2023
Author(s)
Amy Yates, Jacqueline Cavazos, Geraldine Jeckeln, Ying Hu, Eilidh Noyes, Carina Hahn, Alice O'Toole, P. Jonathon Phillips
Forensic facial specialists identify faces more accurately than untrained participants on tests using high quality images of faces. Whether this superiority holds in more challenging conditions is not known. Here, we measured performance for forensic

NIST Explainable AI Workshop Summary

August 25, 2022
Author(s)
P. Jonathon Phillips, Carina Hahn, Peter Fontana, Amy Yates, Matthew Smith
This report represents a summary of the National Institute of Standards and Technology (NIST) Explainable Artificial Intelligence (AI) Workshop, which NIST held virtually on January 26-28, 2021.

Forensic facial examiners vs. super-recognizers: Evaluating behavior beyond accuracy

August 24, 2021
Author(s)
Carina Hahn, Liansheng Larry Tang, Amy Yates, P. Jonathon Phillips
We evaluated the detailed, behavioral properties of face matching performance in two specialist groups: forensic facial examiners and super-recognizers. Both groups compare faces to determine identity with high accuracy and outperform the general

User Guide for NIST Media Forensic Challenge (MFC) Datasets

July 6, 2021
Author(s)
Haiying Guan, Andrew Delgado, Yooyoung Lee, Amy Yates, Daniel Zhou, Timothee N. Kheyrkhah, Jonathan G. Fiscus
NIST released a set of Media Forensic Challenge (MFC) datasets developed in DARPA MediFor (Media Forensics) project to the public in the past 5 years. More than 300 individuals, 150 organizations, from 26 countries and regions worldwide use our datasets

2018 Multimedia Forensics Challenges (MFC18): Summary and Results

November 12, 2020
Author(s)
Yooyoung Lee, Amy Yates, Haiying Guan, Jonathan G. Fiscus, Daniel Zhou
The interest of forensic techniques capable of detecting many different manipulation types has been growing, and system developments with machine learning technology have been evolving in recent years. There has been, however, a lack of diverse data

Media Forensics Challenge Image Provenance Evaluation and State-of-the-Art Analysis on Large-Scale Benchmark Datasets

October 26, 2020
Author(s)
Xiongnan Jin, Yooyoung Lee, Jonathan G. Fiscus, Haiying Guan, Amy Yates, Andrew Delgado, Daniel F. Zhou
With the development of storage, transmission, editing, and sharing tools, digital forgery images are propagating rapidly. The need for image provenance analysis has never been more timely. Typical applications are content tracking, copyright enforcement

NIST Media Forensic Challenge (MFC) Evaluation 2020 - 4th Year DARPA MediFor PI meeting

July 15, 2020
Author(s)
Jonathan G. Fiscus, Haiying Guan, Yooyoung Lee, Amy Yates, Andrew Delgado, Daniel F. Zhou, Timothee N. Kheyrkhah, Xiongnan Jin
The presentation slides summarize NIST Media Forensic Challenge (MFC) Evaluation, and present MFC20 evaluation reports in DARPA MediFor PI meeting. The slides contains five parts: Overview, Image Tasks, Video Tasks, Camera ID Verification Tasks, Provenance