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Countering Malicious DeepFakes: Survey, Battleground, and Horizon

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Abstract

The creation or manipulation of facial appearance through deep generative approaches, known as DeepFake, have achieved significant progress and promoted a wide range of benign and malicious applications, e.g., visual effect assistance in movie and misinformation generation by faking famous persons. The evil side of this new technique poses another popular study, i.e., DeepFake detection aiming to identify the fake faces from the real ones. With the rapid development of the DeepFake-related studies in the community, both sides (i.e., DeepFake generation and detection) have formed the relationship of battleground, pushing the improvements of each other and inspiring new directions, e.g., the evasion of DeepFake detection. Nevertheless, the overview of such battleground and the new direction is unclear and neglected by recent surveys due to the rapid increase of related publications, limiting the in-depth understanding of the tendency and future works. To fill this gap, in this paper, we provide a comprehensive overview and detailed analysis of the research work on the topic of DeepFake generation, DeepFake detection as well as evasion of DeepFake detection, with more than 318 research papers carefully surveyed. We present the taxonomy of various DeepFake generation methods and the categorization of various DeepFake detection methods, and more importantly, we showcase the battleground between the two parties with detailed interactions between the adversaries (DeepFake generation) and the defenders (DeepFake detection). The battleground allows fresh perspective into the latest landscape of the DeepFake research and can provide valuable analysis towards the research challenges and opportunities as well as research trends and future directions. We also elaborately design interactive diagrams (http://www.xujuefei.com/dfsurvey) to allow researchers to explore their own interests on popular DeepFake generators or detectors.

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Notes

  1. https://github.com/clpeng/Awesome-Face-Forgery-Generation-and-Detection.

  2. https://www.kaggle.com/c/deepfake-detection-challenge.

  3. https://competitions.codalab.org/competitions/25228.

  4. http://www.xujuefei.com/dfsurvey.

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Acknowledgements

This research was partly supported by the National Key Research and Development Program of China Under Grant No. 2021YFB3100700, the Fellowship of China National Postdoctoral Program for Innovative Talents Under No. BX2021229, the Natural Science Foundation of Hubei Province Under No. 2021CFB089, the Fundamental Research Funds for the Central Universities Under No. 2042021kf1030, the Open Foundation of Henan Key Laboratory of Cyberspace Situation Awareness under No. HNTS2022004, the National Natural Science Foundation of China (NSFC) under No. 61876134. The work was also supported by the National Research Foundation, Singapore under its the AI Singapore Programme (AISG2-RP-2020-019), the National Research Foundation, Prime Ministers Office, Singapore under its National Cybersecurity R &D Program (No. NRF2018NCR-NCR005-0001), NRF Investigatorship NRFI06-2020-0001, the National Research Foundation through its National Satellite of Excellence in Trustworthy Software Systems (NSOE-TSS) project under the National Cybersecurity R &D (NCR) Grant (No. NRF2018NCR-NSOE003-0001). We gratefully acknowledge the support of NVIDIA AI Tech Center (NVAITC) to our research.

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  1. Alibaba Group, Sunnyvale, CA, USA

    Felix Juefei-Xu

  2. Key Laboratory of Aerospace Information Security and Trust Computing, School of Cyber Science and Engineering, Wuhan University, Wuhan, China

    Run Wang

  3. East China Normal University, Shanghai, China

    Yihao Huang

  4. College of Intelligence and Computing, Tianjin University, Tianjin, China

    Qing Guo

  5. Nanyang Technological University, Singapore, Singapore

    Qing Guo & Yang Liu

  6. Alberta Machine Intelligence Institute (AMII), University of Alberta, Edmonton, AB, Canada

    Lei Ma

  7. Zhejiang Sci-Tech University, Hangzhou, China

    Yang Liu

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Juefei-Xu, F., Wang, R., Huang, Y. et al. Countering Malicious DeepFakes: Survey, Battleground, and Horizon. Int J Comput Vis 130, 1678–1734 (2022). https://doi.org/10.1007/s11263-022-01606-8

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