AIGC时代的深度伪造检测:调查、基准和未来展望

IF 15.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Shichuang Xie , Tong Qiao , Sheng Li , Xinpeng Zhang , Jiantao Zhou , Guorui Feng
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引用次数: 0

摘要

近年来,在数据、计算能力和深度生成模型不断进步的推动下,DeepFake得到了进一步发展。这种新兴的数字媒体伪造技术可以操纵或生成虚假的人脸内容,越来越模糊了真实和虚假媒体之间的界限。随着DeepFake被越来越多地滥用,相关风险也在加剧。虽然对DeepFake检测进行了一些研究,但检测方面的研究明显落后于DeepFake的生成,缺乏对DeepFake检测的全面的、最新的调查。因此,为了有效对抗DeepFake人脸的泛滥,促进DeepFake检测的进化,我们进行了全面的调查和分析。具体而言,(1)分析了推动DeepFake扩散的关键因素,回顾了DeepFake人脸的四种代表性类型,并介绍了一种基于基础模型的跨模态人脸操作方法;(2)重组DeepFake检测方法,建立检测评价基准,强调新兴检测器的潜力;(3)聚焦当前深度伪造取证研究面临的挑战及发展趋势,并提供未来展望,旨在为AIGC时代的深度伪造取证研究提供新的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DeepFake detection in the AIGC era: A survey, benchmarks, and future perspectives
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.
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来源期刊
Information Fusion
Information Fusion 工程技术-计算机:理论方法
CiteScore
33.20
自引率
4.30%
发文量
161
审稿时长
7.9 months
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.
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