1 Ocak 2021 Dergi makalesi Açık Erişim

Altinisik, Enes; Sencar, Husrev Taha

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/234588</identifier>
  <creators>
    <creator>
      <creatorName>Altinisik, Enes</creatorName>
      <givenName>Enes</givenName>
      <familyName>Altinisik</familyName>
    </creator>
    <creator>
      <creatorName>Sencar, Husrev Taha</creatorName>
      <givenName>Husrev Taha</givenName>
      <familyName>Sencar</familyName>
    </creator>
  </creators>
  <titles>
    <title>Source Camera Verification For Strongly Stabilized Videos</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2021</publicationYear>
  <dates>
    <date dateType="Issued">2021-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/234588</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1109/TIFS.2020.3016830</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">Image stabilization performed during imaging and/or post-processing poses one of the most significant challenges to photo-response non-uniformity based source camera attribution from videos. When performed digitally, stabilization involves cropping, warping, and inpainting of video frames to eliminate unwanted camera motion. Hence, successful attribution requires inversion of these transformations in a blind manner. To address this challenge, we introduce a source camera verification method for videos that takes into account spatially variant nature of stabilization transformations and assumes a larger degree of freedom in their search. Our method identifies transformations at a sub-frame level, incorporates a number of constraints to validate their correctness, and offers computational flexibility in the search for the correct transformation. The method also adopts a holistic approach in countering disruptive effects of other video generation steps, such as video coding and downsizing, for more reliable attribution. Tests performed on one public and two custom datasets show that the proposed method is able to verify the source of 23-30% of all videos that underwent stronger stabilization, depending on computation load, without a significant impact on false attribution.</description>
  </descriptions>
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