Decoding Deception: Understanding Human Discrimination Ability in Differentiating Authentic Faces from Deepfake Deceits

Advances in innovative digital technologies present a maturing challenge in differentiating between authentic and manipulated media. The evolution of automated technology has specifically exacerbated this issue, with the emergence of DeepFake content. The degree of sophistication poses potential risks and raise concerns across multiple domains including forensic imagery analysis, especially for Facial Image Comparison (FIC) practitioners. It remains unclear as to whether DeepFake videos can be accurately distinguished from their authentic counterparts, when analysed by domain experts. In response, we present our study where two participant cohorts (FIC practitioners and novice subjects) were shown eleven videos (6 authentic videos and 5 DeepFake videos) and asked to make judgments about the authenticity of the faces. The research findings indicate that when distinguishing between DeepFake and authentic faces, FIC practitioners perform at a similar level to the untrained, novice cohort. Though, statistically, the novice cohort outperformed the practitioners with an overall performance surpassing 70%, relative to the FIC practitioners. This research is still in its infancy stage, yet it is already making significant contributions to the field by facilitating a deeper understanding of how DeepFake content could potentially influence the domain of Forensic Image Identification.