TY - JOUR
T1 - SLM-DFS
T2 - A systematic literature map of deepfake spread on social media
AU - Atlam, El Sayed
AU - Almaliki, Malik
AU - Elmarhomy, Ghada
AU - Almars, Abdulqader M.
AU - Elsiddieg, Awatif M.A.
AU - ElAgamy, Rasha
N1 - Publisher Copyright:
© 2024 The Authors
PY - 2025/1
Y1 - 2025/1
N2 - In recent years, deepfakes (DFs)-realistically manipulated media created using artificial intelligence—have raised significant concerns. As this technology evolves, the urgency for effective detection methods to counter misuse intensifies. Computer science researchers are increasingly focused on stopping the spread of deepfakes (DFs) on social media. However, there has been no comprehensive overview of research in this area. This paper presents a systematic literature map that analyzes research on DF spread on social media from 286 primary studies published between 2018 and June 2024. The studies are categorized by their research type, contribution and focus, revealing a predominant emphasis on detection solutions. Notably, there are significant gaps in evaluating these solutions, using digital interventions to curb dissemination, and managing DF propagation. This literature map will aid researchers, practitioners, and policymakers navigate the rapidly evolving field of DF detection by presenting a structured overview of the available knowledge.
AB - In recent years, deepfakes (DFs)-realistically manipulated media created using artificial intelligence—have raised significant concerns. As this technology evolves, the urgency for effective detection methods to counter misuse intensifies. Computer science researchers are increasingly focused on stopping the spread of deepfakes (DFs) on social media. However, there has been no comprehensive overview of research in this area. This paper presents a systematic literature map that analyzes research on DF spread on social media from 286 primary studies published between 2018 and June 2024. The studies are categorized by their research type, contribution and focus, revealing a predominant emphasis on detection solutions. Notably, there are significant gaps in evaluating these solutions, using digital interventions to curb dissemination, and managing DF propagation. This literature map will aid researchers, practitioners, and policymakers navigate the rapidly evolving field of DF detection by presenting a structured overview of the available knowledge.
KW - A systematic literature
KW - Deepfake image
KW - Deepfake video
KW - Deepfake “DF” detection
KW - Machine learning
KW - Social media
UR - http://www.scopus.com/inward/record.url?scp=85207658344&partnerID=8YFLogxK
U2 - 10.1016/j.aej.2024.10.076
DO - 10.1016/j.aej.2024.10.076
M3 - Article
AN - SCOPUS:85207658344
SN - 1110-0168
VL - 111
SP - 446
EP - 455
JO - Alexandria Engineering Journal
JF - Alexandria Engineering Journal
ER -