Seeing is no longer believing: a study of Deep Fake identification ability and its social impact

This research explores university students ability to identify deepfake images generated by artificial intelligence, and their criminological implications when faced with manipulated content. Through an online questionnaire with 129 participants, the study designed deepfake identification tasks and attitude scales combining demographic, cultural, and experiential factors. The results indicate an overall deepfake identification accuracy of 55.8%, consistent with previous meta-analyses. Regression analysis showed that the education level and AI tool usage experience positively predicted performance, while age and gender had no significant effect. Students who received prior deepfake training had significantly improved identification accuracy; cultural background failed to reach statistical significance. More importantly, exposure to deepfakes reduced trust in digital images and self-judgement confidence. These findings indicate that despite university students high reliance on digital platforms, they are not naturally more resilient to deepfakes and remain susceptible to crimes such as fraud, identity theft, and forged evidence. Overall, this paper demonstrates that deepfakes present novel victimisation risks within criminology while undermining the credibility of digital evidence, highlighting the importance of advancing relevant education and prevention strategies within higher education and criminal sciences.