| 深度伪造生成与检测研究综述 |
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| 引用本文： | 唐玉敏,范菁,曲金帅.深度伪造生成与检测研究综述[J].计算机工程与应用,2022,58(23):56-66. |
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| 作者姓名： | 唐玉敏 范菁 曲金帅 |
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| 作者单位： | 1.云南民族大学 电气信息工程学院，昆明 650500
2.云南省高校通信与信息安全灾备重点实验室，昆明 650500 |
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| 摘 要： | 目前用于建立和操作多媒体信息技术已经发展到了可确保高度真实感的程度。深度伪造作为一种生成式深度学习算法，可实现音频、图像、视频的伪造生成，近些年也取得了相当巨大的进步，与之对抗的深度伪造检测技术也在不断的发展中。梳理常见深度伪造生成的技术以及相关的数据集，总结其中的原理以及最新方法成果；并对深度伪造检测相关技术和数据集进行分析总结。对深度伪造生成和检测的未来研究方向进行了总结和展望。
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| 关 键 词： | 深度伪造 伪造生成 伪造检测 深度学习 发展态势 |
Overview of Deepfake Generation and Detection |
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| TANG Yumin,FAN Jing,QU Jinshuai.Overview of Deepfake Generation and Detection[J].Computer Engineering and Applications,2022,58(23):56-66. |
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| Authors: | TANG Yumin FAN Jing QU Jinshuai |
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| Affiliation: | 1.School of Electrical and Information Technology, Yunnan Minzu University, Kunming 650500, China
2.University Key Laboratory of Information and Communication on Security Backup and Recovery in Yunnan Province, Kunming 650500, China |
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| Abstract: | At present, the technology used to establish and operate multimedia information has been developed to ensure a high degree of realism. As a deep learning algorithm, deepfake can realize the forgery generation of audio, image and video. In recent years, considerable progress has been made, and the anti deepfake detection technology is also developing. It sorts out common deepfake generation technologies and related datasets, and summarizes the principles and the latest method results. The related technologies and datasets of deepfake detection are analyzed and summarized. Finally, the future research directions of deepfake generation and detection are summarized and prospected. |
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| Keywords: | deepfake deepfake generation deepfake detection deep learning development trend |
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