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<article article-type="research-article">
  <front>
    <journal-meta>
      <journal-id journal-id-type="aggregator">72010604</journal-id>
      <journal-title>Electronic Imaging</journal-title>
      <issn pub-type="ppub">2470-1173</issn><issn pub-type="epub"></issn>
      <publisher>
        <publisher-name>Society for Imaging Science and Technology</publisher-name>
        <publisher-loc>7003 Kilworth Lane, Springfield, VA 22151 USA</publisher-loc>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.2352/ISSN.2470-1173.2021.4.MWSF-271</article-id>
      <article-id pub-id-type="sici">2470-1173(20210118)2021:4L.2711;1-</article-id>
      <article-id pub-id-type="publisher-id">ei_24701173_v2021n4_input/s2.xml</article-id>
      <article-id pub-id-type="other">/ist/ei/2021/00002021/00000004/art00002</article-id>
      <article-categories>
        <subj-group>
          <subject>Articles</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Detecting Deepfakes with Haralick’s Texture Properties</article-title>
      </title-group>
      <contrib-group>
        <contrib>
          <name>
            <surname>Frick</surname>
            <given-names>Raphael Antonius</given-names>
          </name>
        </contrib>
        <contrib>
          <name>
            <surname>Zmudzinski</surname>
            <given-names>Sascha</given-names>
          </name>
        </contrib>
        <contrib>
          <name>
            <surname>Steinebach</surname>
            <given-names>Martin</given-names>
          </name>
        </contrib>
      </contrib-group>
      <pub-date>
        <day>18</day>
        <month>01</month>
        <year>2021</year>
      </pub-date>
      <volume>2021</volume>
      <issue>4</issue>
      <fpage>271-1</fpage>
      <lpage>271-7</lpage>
      <permissions>
        <copyright-year>2021</copyright-year>
      </permissions>
      <abstract>
        <p>
          <italic>In the recent years, the detection of deepfake videos has become a major topic in the field of digital media forensics, as the amount of such videos circulating on the internet has drastically risen. Providers of content, such as Facebook and Amazon, have become aware of this new
 threat to spreading misinformation on the Internet. In this work, a novel forgery detection method based on the texture analysis known from image classification and segmentation is proposed. In the experimental results, its performance has shown to be comparable to related works.</italic>
        </p>
      </abstract>
      <kwd-group>
        <kwd>Multimedia forensics</kwd>
        <kwd>Deepfake</kwd>
        <kwd>Haralick features</kwd>
      </kwd-group>
    </article-meta>
  </front>
</article>
