What Looks Good and Familiar Seems Real: How Heuristic Processing of AI-Powered Synthetic and Real Videos Shapes User Perceptions and Detection Accuracy

The media landscape now contains a growing mix of real and synthetic videos, presenting either authentic or false content. Drawing on the heuristic–systematic model, we conducted a mixed-design survey experiment in China to examine how perceived technical quality and content familiarity influence individuals’ perceptions of and performance in detecting real and synthetic videos. Multilevel analyses based on participant evaluations revealed that both perceived technical quality and content familiarity were positively associated with perceived video realness and trust in the vlogger. Higher perceived technical quality improved detection accuracy for real videos but decreased accuracy for synthetic videos. In addition, the positive association between content familiarity and trust in the vlogger was stronger for synthetic videos than for real videos. These findings deepen our understanding of how heuristic processing shapes perceptions and discernment of potentially synthetic videos and offer practical insights for mitigating the risks associated with AI-generated visual deepfakes.