<?xml version="1.0"?>
                    <!DOCTYPE article PUBLIC "-//NLM//DTD Journal Publishing DTD v3.0 20080202//EN" "journalpublishing3.dtd">
                    <article article-type="research-article">
                    <front>
                        <journal-meta>
                        <journal-id journal-id-type="publisher-id">ei</journal-id>
                        <journal-title>Electronic Imaging</journal-title>
                        <issn pub-type="ppub">2470-1173</issn><issn pub-type="epub">2470-1173</issn>
                        <publisher>
                            <publisher-name>Society for Imaging Science and Technology</publisher-name>
                            <publisher-loc>IS&amp;T 7003 Kilworth Lane, Springfield, VA 22151 USA</publisher-loc>
                        </publisher>
                        </journal-meta>
                        <article-meta>
                        <article-id pub-id-type="doi">10.2352/EI.2022.34.4.MWSF-324</article-id>
                        <article-id pub-id-type="publisher-id">MWSF-324</article-id>
                        <article-categories>
                            <subj-group>
                            <subject>Article</subject>
                            </subj-group>
                        </article-categories>
                        <title-group>
                            <article-title>Forensic data model for artificial intelligence based media forensics - Illustrated on the example of DeepFake detection</article-title>
                        </title-group><contrib-group content-type="all"><contrib contrib-type="author"><name>
                                <surname>Siegel</surname>
                                <given-names>Dennis </given-names>
                               </name> <xref ref-type="aff" rid="aff1author1"/></contrib> <aff id="aff1author1">Otto-von-Guericke University Magdeburg, Germany</aff></contrib-group><contrib-group content-type="all"><contrib contrib-type="author"><name>
                                <surname>Krätzer</surname>
                                <given-names>Christian </given-names>
                               </name> <xref ref-type="aff" rid="aff1author2"/></contrib> <aff id="aff1author2">Otto-von-Guericke University Magdeburg, Germany</aff></contrib-group><contrib-group content-type="all"><contrib contrib-type="author"><name>
                                <surname>Seidlitz</surname>
                                <given-names>Stefan </given-names>
                               </name> <xref ref-type="aff" rid="aff1author3"/></contrib> <aff id="aff1author3">Otto-von-Guericke University Magdeburg, Germany</aff></contrib-group><contrib-group content-type="all"><contrib contrib-type="author"><name>
                                <surname>Dittmann</surname>
                                <given-names>Jana </given-names>
                               </name> <xref ref-type="aff" rid="aff1author4"/></contrib> <aff id="aff1author4">Otto-von-Guericke University Magdeburg, Germany</aff></contrib-group><abstract>
                        <title>Abstract</title>
                        <p>The recent development of AI systems and their frequent use for classification problems poses a challenge from a forensic perspective. In many application fields like DeepFake detection, black box approaches such as neural networks are commonly used. As a result, the underlying classification models usually lack explainability and interpretability. 
In order to increase traceability of AI decisions and move a crucial step further towards precise &amp; reproducible analysis descriptions and certifiable investigation procedures, in this paper a domain adapted forensic data model is introduced for media forensic investigations focusing on media forensic object manipulation detection, such as DeepFake detection.</p>
                        </abstract><pub-date>
                            <day>16</day>
                            <month>01</month>
                            <year>2022</year>
                            </pub-date><volume>34</volume>
                        <issue-acronym>MWSF</issue-acronym>
                        <issue>4</issue>
                        <fpage>324-1</fpage>
                        <lpage>324-6</lpage>
                        <permissions>
                             <copyright-statement>This work is licensed under the Creative Commons Attribution 4.0 International License.  To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.</copyright-statement>
                            <copyright-year>2022</copyright-year>
                        </permissions><kwd-group><kwd>Media Forensics</kwd><kwd> Forensic Data Model</kwd><kwd> DeepFake Detection</kwd><kwd> Explainable Artificial Intelligence</kwd></kwd-group></article-meta>
                    </front>
                    </article>