Gan Pei * . Jiangning Zhang * . Menghan Hu . Zhenyu Zhang . Chengjie Wang . Yunsheng Wu.
Guangtao Zhai . Jian Yang . Chunhua Shen . Dacheng Tao
This work focuses on the aspect of facial manipulation in Deepfake, encompassing Face Swapping, Face Reenactment, Talking Face Generation, Face Attribute Editing and Forgery Detection. We believe this will be the most comprehensive survey to date on facial manipulation and detection technologies. Please stay tuned!😉😉😉
✨You are welcome to provide us your work with a topic related to deepfake generation or detection!!!
If you discover any missing work or have any suggestions, please feel free to submit a pull request or contact us. We will promptly add the missing papers to this repository.
[1] A comprehensive survey for visual Deepfake, including Deepfake generation/detection.
[2] It also contains several related domains, including Heas Swapping, Face Super-resolution, Face Reconstruction, Face Inpainting, Body Animation, Portrait Style Transfer, Makeup Transfer and Adversarial Sample Detection.
[3] We list detailed results for the most representative works.
This work presents a detailed survey on generation and detection tasks about face-related generation, including Face Swapping, Face Reenactment, Talking Face Generation, and Face Attribute Editing. In addition, we also introduce several related fields such as Head Swap, Face Super-resolution, Face Reconstruction, Face Inpainting, etc., and select some of them to expand.
If you find our survey and repository useful for your research project, please consider citing our paper:
@article{pei2024deepfake,
title={Deepfake Generation and Detection: A Benchmark and Survey},
author={Pei, Gan and Zhang, Jiangning and Hu, Menghan and Zhang, Zhenyu and Wang, Chengjie and Wu, Yunsheng and Zhai, Guangtao and Yang, Jian and Shen, Chunhua and Tao, Dacheng},
journal={arXiv preprint arXiv:2403.17881},
year={2024}
}
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