The rise of video generative models that produce high-quality content has made it increasingly difficult to discern video authenticity. AI-extended videos, which mix real-world footage with generative content, pose new challenges in distinguishing real from manipulated segments. AI-extended videos might be utilized to deceive humans, but they also have the capacity to assist video creators and offer people novel video experiences. Despite these concerns, research on how people recognize and evaluate AI-extended videos remains limited. To address this, we conducted a user study where participants interacted with AI-extended videos on a web-based system, identifying boundaries between raw and generated content, followed by a survey and one-on-one interviews. Our quantitative and qualitative analyses revealed how individuals perceive these videos, the factors influencing their perception, evaluations and attitudes. We believe that these insights will aid the future development of AI-extended video technologies and ecosystems.
https://dl.acm.org/doi/10.1145/3706598.3714061
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)