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Face Forgery Detection via Texture and Saliency Enhancement

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MultiMedia Modeling (MMM 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14554))

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Abstract

In recent years, AI-driven advancements have resulted in increasingly sophisticated face forgery techniques, posing a challenge in distinguishing genuine images from manipulated ones. This presents significant societal and trust-related concerns. Most current methods approach this task as a binary classification problem via CNN, which potentially overlooks subtle forgery cues due to the down-sampling operations. This may lead to inadequate extraction of discriminative features. In this paper, we argue that forgery clues are hidden within facial textures and salient regions. Motivated by this insight, we propose the Texture and Saliency Enhancement Network (TSE-Net). TSE-Net contains two primary components: the Dynamic Texture Enhancement Module (DTEM) and the Salient Region Attention Module (SRAM). DTEM employs the Gray-Level Co-occurrence Matrix to extract facial texture information, while SRAM concentrates on salient regions. Extensive experiments demonstrate that the TSE-Net has superior performance in comparison to competing methods, emphasizing its effectiveness in detecting subtle face forgery cues through enhanced texture and saliency information extraction.

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Acknowledgments

This work was supported by National Key R & D Program of China (No. 2022ZD0118202), the National Science Fund for Distinguished Young Scholars (No. 62025603), the National Natural Science Foundation of China (No. U21B2037, No. U22B2051, No. 62176222, No. 62176223, No. 62176226, No. 62072386, No. 62072387, No. 62072389, No. 62002305 and No. 62272401), and the Natural Science Foundation of Fujian Province of China (No. 2021J01002, No. 2022J06001).

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Authors and Affiliations

  1. Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, Xiamen, 361005, People’s Republic of China

    Sizheng Guo, Haozhe Yang & Xianming Lin

Authors
  1. Sizheng Guo
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  2. Haozhe Yang
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  3. Xianming Lin
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Corresponding author

Correspondence to Xianming Lin .

Editor information

Editors and Affiliations

  1. University of Amsterdam, Amsterdam, The Netherlands

    Stevan Rudinac

  2. Delft University of Technology, Delft, The Netherlands

    Alan Hanjalic

  3. Delft University of Technology, Delft, The Netherlands

    Cynthia Liem

  4. University of Amsterdam, Amsterdam, The Netherlands

    Marcel Worring

  5. Reykjavik University, Reykjavik, Iceland

    Björn Þór Jónsson

  6. Microsoft Research Lab – Asia, Beijing, China

    Bei Liu

  7. The University of Tokyo, Tokyo, Japan

    Yoko Yamakata

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Guo, S., Yang, H., Lin, X. (2024). Face Forgery Detection via Texture and Saliency Enhancement. In: Rudinac, S., et al. MultiMedia Modeling. MMM 2024. Lecture Notes in Computer Science, vol 14554. Springer, Cham. https://doi.org/10.1007/978-3-031-53305-1_37

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  • DOI: https://doi.org/10.1007/978-3-031-53305-1_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-53304-4

  • Online ISBN: 978-3-031-53305-1

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