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This study presents a unified evaluation pipeline for post hoc visual explanations, grounded in the Co\u201012 attributes of explanation quality and operationalised through a structured framework that assesses Coherence, Composition, Correctness, Completeness, Compactness, Covariate completeness and Continuity. Applying this pipeline to 16 representative explanation methods reveals systematic differences across methodological categories. CAM\u2010based approaches demonstrate strong spatial coherence and temporal continuity; gradient\u2010based techniques such as Guided Backprop and LRP yield compact and accurate attributions; and redistribution\u2010based methods including ExcitationBP and Deep Taylor maintain high consistency across evaluation conditions. In contrast, perturbation\u2010based approaches such as SHAP and LIME exhibit weaker localisation and reduced temporal stability. By enabling controlled, attribute\u2010level comparison of explanation methods, the proposed pipeline bridges conceptual interpretability frameworks and empirical analysis, offering practical guidance for the development and deployment of interpretable deepfake detectors in forensic and auditing applications. The source code is publicly available at:\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/junxinchenieee\/EAI-Deepfake-Detection\">https:\/\/github.com\/junxinchenieee\/EAI\u2010Deepfake\u2010Detection<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1111\/exsy.70222","type":"journal-article","created":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T07:05:37Z","timestamp":1770275137000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Explainable Artificial Intelligence for Deepfake Detection: Pipeline, Open Source and Comparisons"],"prefix":"10.1111","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-6093-0740","authenticated-orcid":false,"given":"Hao","family":"Li","sequence":"first","affiliation":[{"name":"School of Software Dalian University of Technology  Dalian China"},{"name":"School of Computing Science University of Glasgow  Glasgow UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4745-8361","authenticated-orcid":false,"given":"Junxin","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Software Dalian University of Technology  Dalian China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1057-7973","authenticated-orcid":false,"given":"Bo","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering Dalian University of Technology  Dalian China"}]},{"given":"Salvador","family":"Garc\u00eda","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI)  Granada Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5051-3475","authenticated-orcid":false,"given":"David","family":"Camacho","sequence":"additional","affiliation":[{"name":"School of Computer Systems Engineering Universidad Politecnica de Madrid  Madrid Spain"}]}],"member":"311","published-online":{"date-parts":[[2026,2,4]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2870052"},{"key":"e_1_2_10_3_1","first-page":"9525","volume-title":"Proceedings of the 32nd International Conference on Neural Information Processing Systems (NeurIPS)","author":"Adebayo J.","year":"2018"},{"key":"e_1_2_10_4_1","doi-asserted-by":"crossref","unstructured":"Agarwal C. 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