DeepFake video detection using time-series information

DOI

Bibliographic Information

Other Title
  • 時系列情報を用いたDeepFake動画の検知

Description

<p>In recent years, an image generation method using deep learning such as GAN or VAE can generate a high-definition image that does not actually exist. Also, by applying such an image generation technique, it is possible to convert an arbitrary image. By applying these technologies and converting face images in a moving image, it is possible to generate a FAKE videos that cannot be distinguished from reality. FAKE videos generated by manipulating facial images have spread on news sites and social networks, and their use as politics and pornography has become a social issue. Therefore, it is very important to develop a method for detecting whether a Real video or generated videos. Many of the detection methods focus on the features of the face image in each frame unit in the FAKE video, but the identification of a single face image has become difficult due to the sophistication of the generation method. Therefore, our proposed method focuses on the relationship between faces in each frame in the FAKE video, and identifies them based on the time evolution information of the faces. We verify the data that was difficult to identify with the existing method, and show the validity of the proposed method.</p>

Journal

Keywords

Details 詳細情報について

  • CRID
    1390003825189274368
  • NII Article ID
    130007856752
  • DOI
    10.11517/pjsai.jsai2020.0_1n3gs1002
  • ISSN
    27587347
  • Text Lang
    ja
  • Data Source
    • JaLC
    • CiNii Articles
  • Abstract License Flag
    Disallowed

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