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Deepfake generation and detection, a survey

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Abstract

Deepfake refers to realistic, but fake images, sounds, and videos generated by articial intelligence methods. Recent advances in deepfake generation make deepfake more realistic and easier to make. Deepfake has been a signicant threat to national security, democracy, society, and our privacy, which calls for deepfake detection methods to combat potential threats. In the paper, we make a survey on state-ofthe-art deepfake generation methods, detection methods, and existing datasets. Current deepfake generation methods can be classified into face swapping and facial reenactment. Deepfake detection methods are mainly based features and machine learning methods. There are still some challenges for deepfake detection, such as progress on deepfake generation, lack of high quality datasets and benchmark. Future trends on deepfake detection can be efficient, robust and systematical detection methods and high quality datasets.

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Acknowledgements

This work was supported by Guangxi Key Laboratory of Cryptography and Information Security (GCIS201806), Guangxi Key Laboratory of Trusted Software (No. kx202016), Key Lab of Film and TV Media Technology of Zhejiang Province (No.2020E10015), Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis, Guangxi University for Nationalities(GXIC20-03), Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province(obdma202001). We’d like to thank Zelei Cheng from Purdue University and Yingjie Wang from Virginia Tech for writing assistance, language editing, and proofreading.

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  1. School of Cyber Science and Technology, Beihang University, Beijing, China

    Tao Zhang

  2. Guangxi Key Laboratory of Cryptography and Information Security, Guilin University of Electronic Technology, Guilin, China

    Tao Zhang

  3. Guangxi Key Laboratory of Trusted Software & Guangxi Key Laboratory of Cryptography and Information Security, Guilin University of Electronic Technology, Guilin, China

    Tao Zhang

  4. Key Lab of Film and TV Media Technology of Zhejiang Province, Hangzhou, China

    Tao Zhang

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Zhang, T. Deepfake generation and detection, a survey. Multimed Tools Appl 81, 6259–6276 (2022). https://doi.org/10.1007/s11042-021-11733-y

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