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<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">JERA</journal-id><journal-title-group><journal-title>Journal of Electronic Research and Application</journal-title></journal-title-group><issn>2208-3502</issn><eissn>2208-3510</eissn><publisher><publisher-name>Bio-Byword Scientific Publishing Pty. Ltd.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26689/jera.v10i1.13911</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>Application Research of Concept Bottleneck Model in Passport Printing Method Detection</title><url>https://artdesignp.com/journal/JERA/10/1/10.26689/jera.v10i1.13911</url><author>QiuTianrui,XuJiafeng</author><pub-date pub-type="publication-year"><year>2026</year></pub-date><volume>10</volume><issue>1</issue><history><date date-type="pub"><published-time>2026-02-12</published-time></date></history><abstract>With the increase in cross-border mobility, passports, as critical identity documents, require robust anti-counterfeiting security. While existing deep learning-based automatic detection methods achieve high accuracy, they lack interpretability. This paper introduces the Concept Bottleneck Model (CBM) to construct a transparent passport printing method detection framework. By defining interpretable intermediate concepts and integrating linear reasoning, the model significantly enhances reliability and debugging efficiency. The article systematically analyzes the advantages, challenges, and future directions of this approach.</abstract><keywords/></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>Weeraratna T, 2024, Beyond Borders: The Art and Science of Detecting Travel Document Forgeries. International Journal of Forensic Sciences, 9(4): 1–4.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B2" content-type="article"><label>2</label><element-citation publication-type="journal"><p>Mohit M, 2016, The Evolution of Deep Learning: A Performance Analysis of CNNs in Image Recognition. International Journal of Advance Research in Education and Technology, 3(6): 2029–2038.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B3" content-type="article"><label>3</label><element-citation publication-type="journal"><p>Stropeni A, Enhancing Interpretability in Visual Anomaly Detection Through Concept Bottleneck Models, thesis, University of Padua.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B4" content-type="article"><label>4</label><element-citation publication-type="journal"><p>Dhillon A, Verma G, 2020, Convolutional Neural Network: A Review of Models, Methodologies and Applications to Object Detection. Progress in Artificial Intelligence, 9(2): 85–112.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B5" content-type="article"><label>5</label><element-citation publication-type="journal"><p>Shafik W, 2026, The “Black Box” Problem: Lack of Transparency in AI Decision-Making. Springer: 167–186.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B6" content-type="article"><label>6</label><element-citation publication-type="journal"><p>Srivastava D, Yan G, Weng L, et al., 2024, VLG-CBM: Training Concept Bottleneck Models with Vision–Language Guidance. Advances in Neural Information Processing Systems, 37: 79057–79094.</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
