{"title":"SLM-DFS:社交媒体上深度伪造传播的系统文献图谱","authors":"","doi":"10.1016/j.aej.2024.10.076","DOIUrl":null,"url":null,"abstract":"<div><div>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. The findings of this literature map suggest that DF detection is a multidisciplinary field that requires collaboration between experts in computer vision, machine learning, cybersecurity, and media forensics to address its current and future challenges</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":null,"pages":null},"PeriodicalIF":6.2000,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"SLM-DFS: A systematic literature map of deepfake spread on social media\",\"authors\":\"\",\"doi\":\"10.1016/j.aej.2024.10.076\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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. The findings of this literature map suggest that DF detection is a multidisciplinary field that requires collaboration between experts in computer vision, machine learning, cybersecurity, and media forensics to address its current and future challenges</div></div>\",\"PeriodicalId\":7484,\"journal\":{\"name\":\"alexandria engineering journal\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.2000,\"publicationDate\":\"2024-10-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"alexandria engineering journal\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1110016824012420\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"alexandria engineering journal","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1110016824012420","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
SLM-DFS: A systematic literature map of deepfake spread on social media
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. The findings of this literature map suggest that DF detection is a multidisciplinary field that requires collaboration between experts in computer vision, machine learning, cybersecurity, and media forensics to address its current and future challenges
期刊介绍:
Alexandria Engineering Journal is an international journal devoted to publishing high quality papers in the field of engineering and applied science. Alexandria Engineering Journal is cited in the Engineering Information Services (EIS) and the Chemical Abstracts (CA). The papers published in Alexandria Engineering Journal are grouped into five sections, according to the following classification:
• Mechanical, Production, Marine and Textile Engineering
• Electrical Engineering, Computer Science and Nuclear Engineering
• Civil and Architecture Engineering
• Chemical Engineering and Applied Sciences
• Environmental Engineering