Human detection of machine-manipulated media

By Matthew Groh, Ziv Epstein, Nick Obradovich, Manuel Cebrian, Iyad Rahwan. 2019

25 citations recorded by the source corpus.

Technologies for manipulating and faking online media may outpace people's ability to tell the difference.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Sociology and Political Science, Safety, Robustness & Interpretability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Luke S. Zettlemoyer
2. Jeff Wu
3. Irene Solaiman
4. Alec Radford
5. Jianfeng Gao
6. Jack Clark
7. Mohit Bansal
8. Yejin Choi

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Trevor Darrell
3. Dacheng Tao
4. Ming-Hsuan Yang
5. Jitendra Malik
6. Xiaogang Wang
7. Andrea Vedaldi
8. Andrew Zisserman

In Sociology and Political Science

1. Amanda Askell
2. Ariel Herbert-Voss
3. Irene Solaiman
4. Jack Clark
5. Jasmine Wang
6. Miles Brundage
7. Alec Radford
8. Jeff Wu

In Safety, Robustness & Interpretability

1. Irene Solaiman
2. Jack Clark
3. Alec Radford

Citations in the corpus

• Release Strategies and the Social Impacts of Language Models (2019, methodology): “We are also encouraged by related work in machine-manipulated images by Groh et al. [26]. at MIT and the Max Planck Institute.”