{"title":"DeepFake detection in the AIGC era: A survey, benchmarks, and future perspectives","authors":"Shichuang Xie , Tong Qiao , Sheng Li , Xinpeng Zhang , Jiantao Zhou , Guorui Feng","doi":"10.1016/j.inffus.2025.103740","DOIUrl":null,"url":null,"abstract":"<div><div>In recent years, DeepFake has further developed, driven by continuous advances in data, computing power, and deep generative models. This emerging digital media forgery technique can manipulate or generate fake face content, increasingly blurring the boundaries between real and fake media. With the growing misuse of DeepFake, the associated risks are also intensifying. Although some research on DeepFake detection has been conducted, the research on detection is obviously falling behind DeepFake generation, and there is a lack of comprehensive and up-to-date surveys on DeepFake detection. Therefore, to effectively counter the proliferation of DeepFake face and promote the evolution of DeepFake detection, we conduct comprehensive survey and analysis. Specifically, (1) we analyze the key factors driving the proliferation of DeepFake, and we review the four representative types of DeepFake face and introduce a novel cross-modal face manipulation based on foundation models; (2) we reorganize DeepFake detection methods and establish a detection evaluation benchmark, emphasizing the potential of emerging detectors; (3) we focus on the current challenges of DeepFake forensic research and the corresponding development trends, and provide future perspectives, aiming to provide new insights for DeepFake forensic research in the AIGC era.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"127 ","pages":"Article 103740"},"PeriodicalIF":15.5000,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Fusion","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1566253525008024","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
In recent years, DeepFake has further developed, driven by continuous advances in data, computing power, and deep generative models. This emerging digital media forgery technique can manipulate or generate fake face content, increasingly blurring the boundaries between real and fake media. With the growing misuse of DeepFake, the associated risks are also intensifying. Although some research on DeepFake detection has been conducted, the research on detection is obviously falling behind DeepFake generation, and there is a lack of comprehensive and up-to-date surveys on DeepFake detection. Therefore, to effectively counter the proliferation of DeepFake face and promote the evolution of DeepFake detection, we conduct comprehensive survey and analysis. Specifically, (1) we analyze the key factors driving the proliferation of DeepFake, and we review the four representative types of DeepFake face and introduce a novel cross-modal face manipulation based on foundation models; (2) we reorganize DeepFake detection methods and establish a detection evaluation benchmark, emphasizing the potential of emerging detectors; (3) we focus on the current challenges of DeepFake forensic research and the corresponding development trends, and provide future perspectives, aiming to provide new insights for DeepFake forensic research in the AIGC era.
期刊介绍:
Information Fusion serves as a central platform for showcasing advancements in multi-sensor, multi-source, multi-process information fusion, fostering collaboration among diverse disciplines driving its progress. It is the leading outlet for sharing research and development in this field, focusing on architectures, algorithms, and applications. Papers dealing with fundamental theoretical analyses as well as those demonstrating their application to real-world problems will be welcome.