Publications (24)
Characterizing Photorealism and Artifacts in Diffusion Model-Generated Images
Negar Kamali, Karyn Nakamura, Aakriti Kumar +3
Diffusion model-generated images can appear indistinguishable from authentic photographs, but these images often contain artifacts and implausibilities that reveal their AI-generat…
Deepfake Detection by Human Crowds, Machines, and Machine-informed Crowds
Matthew Groh, Ziv Epstein, Chaz Firestone +1
The recent emergence of machine-manipulated media raises an important societal question: how can we know if a video that we watch is real or fake? In two online studies with 15,016…
Social influence leads to the formation of diverse local trends
Ziv Epstein, Matthew Groh, Abhimanyu Dubey +1
How does the visual design of digital platforms impact user behavior and the resulting environment? A body of work suggests that introducing social signals to content can increase…
Generative AI Literacy Training Improves Intelligence Analysts' Discrimination of Real and AI-Generated Images
Negar Kamali, Candice Rockell Gerstner, Jessica Hullman +1
Across social and online platforms, people are increasingly exposed to AI-generated images. As a consequence, the task of distinguishing AI-generated from authentic images is becom…
Deceptive AI systems that give explanations are more convincing than honest AI systems and can amplify belief in misinformation
Valdemar Danry, Pat Pataranutaporn, Matthew Groh +2
Advanced Artificial Intelligence (AI) systems, specifically large language models (LLMs), have the capability to generate not just misinformation, but also deceptive explanations t…
Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Dataset
Matthew Groh, Caleb Harris, Luis Soenksen +5
How does the accuracy of deep neural network models trained to classify clinical images of skin conditions vary across skin color? While recent studies demonstrate computer vision…