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Hybrid Spatio-Temporal Network for Face Forgery Detection

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Pattern Recognition (ACPR 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14408))

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Abstract

Facial manipulation techniques have aroused increasing security concerns, leading to various methods to detect forgery videos. However, existing methods suffer from a significant performance gap compared to image manipulation methods, partially because the spatio-temporal information is not well explored. To address the issue, we introduce a Hybrid Spatio-Temporal Network (HSTNet) to integrate spatial and temporal information in the same framework. Specifically, our HSTNet utilizes a hybrid architecture, which consists of a 3D CNN branch and a transformer branch, to jointly learn short- and long-range relations in the spatio-temporal dimension. Due to the feature misalignment between the two branches, we design a Feature Alignment Block (FAB) to recalibrate and efficiently fuse heterogeneous features. Moreover, HSTNet introduces a Vector Selection Block (VSB) to combine the outputs of the two branches and fire important features for classification. Extensive experiments show that HSTNet obtains the best overall performance over state-of-the-art methods.

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Acknowledegments

This work was supported by “One Thousand Plan” projects in Jiangxi Province Jxsg2023102268 and National Key Laboratory on Automatic Target Recognition 220402.

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Authors and Affiliations

  1. Beihang University, Beijing, China

    Xuhui Liu, Sicheng Gao, Peizhu Zhou, Xiaoyan Luo & Baochang Zhang

  2. Shenzhen Institute of Advanced Technology, Shenzhen, China

    Jianzhuang Liu

  3. National University of Defense Technology, Changsha, China

    Luping Zhang

  4. Nanchang Institute of Technology, Nanchang, China

    Baochang Zhang

Authors
  1. Xuhui Liu
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  2. Sicheng Gao
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  3. Peizhu Zhou
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  4. Jianzhuang Liu
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  5. Xiaoyan Luo
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  6. Luping Zhang
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  7. Baochang Zhang
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Corresponding author

Correspondence to Baochang Zhang .

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Editors and Affiliations

  1. Kyushu Institute of Technology, Kitakyushu, Fukuoka, Japan

    Huimin Lu

  2. The University of Sydney, Sydney, NSW, Australia

    Michael Blumenstein

  3. Yonsei University, Seoul, Korea (Republic of)

    Sung-Bae Cho

  4. Chinese Academy of Sciences, Bejing, China

    Cheng-Lin Liu

  5. Osaka University, Osaka, Ibaraki, Japan

    Yasushi Yagi

  6. Kyushu Institute of Technology, Kitakyushu, Japan

    Tohru Kamiya

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Liu, X. et al. (2023). Hybrid Spatio-Temporal Network for Face Forgery Detection. In: Lu, H., Blumenstein, M., Cho, SB., Liu, CL., Yagi, Y., Kamiya, T. (eds) Pattern Recognition. ACPR 2023. Lecture Notes in Computer Science, vol 14408. Springer, Cham. https://doi.org/10.1007/978-3-031-47665-5_21

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  • DOI: https://doi.org/10.1007/978-3-031-47665-5_21

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