Unmasking media illusion: analytical survey of deepfake video detection and emotional insights

Abstract

This exploratory study investigates the psychological and demographic factors influencing the ability to detect deepfake videos and examines the broader societal implications of deepfake proliferation. A comprehensive survey administered to 71 participants from two Midwestern universities collected data on psychological and cognitive factors, digital literacy levels, demographics, and deepfake detection abilities. The survey incorporated deepfake and genuine videos to assess participants' identification accuracy and confidence levels. Machine learning (ML) techniques were employed to analyze the data, revealing significant correlations between digital literacy, cognitive biases, and deepfake detection performance. The findings provide a foundation for understanding the complex interplay of psychological, demographic, and technological factors in the fight against deepfakes, emphasizing the importance of considering human dimensions in developing effective countermeasures. Therefore, they underscore the importance of enhancing public awareness and resilience against deepfakes to safeguard information integrity, personal privacy, and trust in digital media.