Deepfakes refers to a wide range of computer-generated synthetic media, in which a person's appearance or likeness is altered to resemble that of another. This paper provides a comprehensive review of the literature on people's ability to detect deepfakes. Five databases (IEEE, ProQuest, PubMed, Web of Science and Scopus) were searched up to December 2023. Forty independent studies from 30 unique records were included in the review. Detection performance varied widely across studies. Generally, high-quality deepfakes are harder to detect, and audio deepfakes pose a significant challenge due to the lack of visual cues. However, studies use various performance metrics such as accuracy rating, AUC, and Likert scales, making it difficult to compare results across studies. Detection accuracy varies widely, with some studies showing humans outperforming AI models and others indicating the opposite. Our review also found that detection performance is influenced by person-level (e.g., cognitive ability, analytical thinking) and stimuli-level factors (e.g., quality of deepfake, familiarity with the subject). Interventions to improve people's deepfake detection yielded mixed results. We also found that humans and AI-based detection models focus on different aspects when detecting, suggesting a potential for human-AI collaboration. The findings highlight the complex interplay of factors influencing human deepfake detection and the need for further research to develop effective strategies for deepfake detection. learn more on SSRN