“He looks very real”: media, knowledge, and search-based strategies for deepfake identification

Abstract

Deepfakes are a potential source of disinformation and the ability to detect them is imperative. While research focused on algorithmic detection methods, there is little work conducted on how people identify deepfakes. This research attempts to fill this gap. Using semi-structured interviews, participants were asked to identify real and deepfake videos and explain how their decisions were made. Three categories of deepfake identification strategies emerged: the use of surface video and audio cues, processing of the messages conveyed in the video, and the searching of external sources. Participants often used multiple strategies within each category. However, identification challenges occurred due to participants' preconceived notions of deepfake characteristics and the message embodied in the video. This work contributes to research by shifting the focus from the algorithmic detection of deepfakes to human-oriented strategies. Practically, the findings provide guidance on how people can identify deepfakes, which can also form the basis for the development of educational materials.