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Intra-variance Guided Metric Learning for Face Forgery Detection

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Biometric Recognition (CCBR 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14463))

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Abstract

Since facial manipulation technology has raised serious concerns, facial forgery detection has also attracted increasing attention. Although recent work has made good achievements, the detection of unseen fake faces is still a big challenge. In this paper, we tackle facial forgery detection problem from the perspective of distance metric learning, and design a new Intra-Variance guided Metric Learning (IVML) method to drive classification and adopt Vision Transformer (ViT) as the backbone, which aims to improve the generalization ability of face forgery detection methods. Specifically, considering that there is a large gap between different real faces, our proposed IVML method increases the distance between real and fake faces while maintaining a certain distance within real faces. We choose ViT as the backbone as our experiments prove that ViT has better generalization ability in face forgery detection. A large number of experiments demonstrate the effectiveness and superiority of our IVML method in cross-dataset evaluation.

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Notes

  1. 1.

    https://github.com/deepfakes/faceswap.

  2. 2.

    https://github.com/MarekKowalski/FaceSwap.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China under Grant 62006013.

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Authors and Affiliations

  1. School of Software, Beihang University, Beijing, China

    Zhentao Chen & Junlin Hu

Authors
  1. Zhentao Chen
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  2. Junlin Hu
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Corresponding author

Correspondence to Junlin Hu .

Editor information

Editors and Affiliations

  1. Hefei University of Technology, Hefei, China

    Wei Jia

  2. South China University of Technology, Guangzhou, China

    Wenxiong Kang

  3. China University of Mining and Technology, Xuzhou, China

    Zaiyu Pan

  4. Shandong University, Jinan, China

    Xianye Ben

  5. China University of Mining and Technology, Xuzhou, China

    Zhengfu Bian

  6. Southern University of Science and Technology, Shenzhen, China

    Shiqi Yu

  7. Chinese Academy of Sciences, Beijing, China

    Zhaofeng He

  8. China University of Mining and Technology, Xuzhou, China

    Jun Wang

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Chen, Z., Hu, J. (2023). Intra-variance Guided Metric Learning for Face Forgery Detection. In: Jia, W., et al. Biometric Recognition. CCBR 2023. Lecture Notes in Computer Science, vol 14463. Springer, Singapore. https://doi.org/10.1007/978-981-99-8565-4_14

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  • DOI: https://doi.org/10.1007/978-981-99-8565-4_14

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8564-7

  • Online ISBN: 978-981-99-8565-4

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