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Deepfake Detection Using Multiple Facial Features

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Advances in Digital Forensics XIX (DigitalForensics 2023)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 687))

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Abstract

Deepfake digital forgery techniques leverage deep learning to replace faces and modify facial expressions in images and videos. The techniques have been used to produce fake pornography, spread fake news and rumors, influence public opinion and even elections. However, deepfake detection techniques are well behind deepfake generation technology.

This chapter describes a deepfake video detection method that leverages aspect ratios to express multiple facial features. The aspect ratios of facial features are computed for every frame in a video and a time window is used to segment processed frame sequences into multiple short segments, following which pattern matching is employed to identify abnormal expressions that are indicative of deepfakes. Experiments with the FaceForensics++ and Celeb-DF datasets reveal that the proposed method detects deepfake videos effectively. Moreover, the aspect ratio computations improve the ability to detect compressed deepfake videos.

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Author information

Authors and Affiliations

  1. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

    Xinzhe Wang, Duohe Ma, Liming Wang, Zhitong Lu, Zhenchao Zhang & Junye Jiang

Authors
  1. Xinzhe Wang
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  2. Duohe Ma
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  3. Liming Wang
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  4. Zhitong Lu
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  5. Zhenchao Zhang
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  6. Junye Jiang
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Corresponding author

Correspondence to Duohe Ma .

Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, Air Force Institute of Technology, Wright-Patterson AFB, OH, USA

    Gilbert Peterson

  2. Keplinger Hall 3315, University of Tulsa, Tulsa, OK, USA

    Sujeet Shenoi

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Wang, X., Ma, D., Wang, L., Lu, Z., Zhang, Z., Jiang, J. (2023). Deepfake Detection Using Multiple Facial Features. In: Peterson, G., Shenoi, S. (eds) Advances in Digital Forensics XIX. DigitalForensics 2023. IFIP Advances in Information and Communication Technology, vol 687. Springer, Cham. https://doi.org/10.1007/978-3-031-42991-0_9

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  • DOI: https://doi.org/10.1007/978-3-031-42991-0_9

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  • Online ISBN: 978-3-031-42991-0

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