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                <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.2024.36.4.MWSF-342</article-id>
                    <article-id pub-id-type="publisher-id">MWSF-342</article-id>
                    <article-categories>
                        <subj-group>
                        <subject>Proceedings Paper</subject>
                        </subj-group>
                    </article-categories>
                    <title-group>
                        <article-title>DeepMammo: Deep Learning Algorithm for Digital Mammogram Source Identification</article-title>
                    </title-group><contrib-group content-type="all"><contrib contrib-type="author"><name>
                            <surname>Mohammadi</surname>
                            <given-names>Farid Ghareh</given-names>
                           </name> <xref ref-type="aff" rid="aff1author1"/></contrib><aff id="aff1author1">Mayo Clinic, US</aff></contrib-group><contrib-group content-type="all"><contrib contrib-type="author"><name>
                            <surname>Sebro</surname>
                            <given-names>Ronnie </given-names>
                           </name> <xref ref-type="aff" rid="aff1author2"/></contrib><aff id="aff1author2">Mayo Clinic, US</aff></contrib-group><abstract>
                    <title>Abstract</title>
                    <p>Advances in AI allow for fake image creation. These techniques can be used to fake mammograms. This could impact patient care and medicolegal cases. One method to verify that an image is original is to confirm the source of the image. A deep-learning algorithm(DeepMammo)-based on CNNs and FCNNs, used to identify the machine that created any mammogram. We analyze mammograms of 1574 patients obtained on 7-different mammography machines and randomly split the dataset by patient into training/validation(80%) and test(20%) datasets. DeepMammo has an accuracy of 98.09%, AUC of 95.96% in the test dataset.</p>
                    </abstract><pub-date>
                        <day>21</day>
                        <month>1</month>
                        <year>2024</year>
                        </pub-date><volume>36</volume>
                    <issue-acronym>MWSF</issue-acronym>
                    <issue-title>Media Watermarking, Security, and Forensics 2024</issue-title>
                    <issue seq="342">4</issue>
                    <fpage>342-1</fpage>
                    <lpage>342-5</lpage>
                    <permissions>
                         <copyright-statement>© 2024, Society for Imaging Science and Technology</copyright-statement>
                        <copyright-year>2024</copyright-year>
                    </permissions><kwd-group><kwd>Deep Learning</kwd><kwd>Forensic Science</kwd><kwd>Machine Learning</kwd><kwd>Mammograms</kwd><kwd>Medical Imaging</kwd><kwd>Radiology</kwd></kwd-group></article-meta>
                </front>
                </article>