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Appl."],"published-print":{"date-parts":[[2024,11,30]]},"abstract":"<jats:p>Deepfake detection has become increasingly important in recent years owing to the widespread availability of deepfake generation technologies. Existing deepfake detection methods present two primary limitations; i.e., they are trained on a specific type of deepfake dataset, which renders them vulnerable to unseen deepfakes, and they regard deepfakes as a \u201cblack box\u201d with limited explainability, making it difficult for non-AI experts to understand and trust the decisions. Hence, this article proposes a novel neurosymbolic deepfake detection framework that exploits the fact that human emotions cannot be imitated easily owing to their complex nature. We argue that deep fakes typically exhibit inter- or intra-modality inconsistencies in the emotional expressions of the person being manipulated. Thus, the proposed framework performs inter- and intra-modality reasoning on emotions extracted from audio and visual modalities using a psychological and arousal-valence model for deepfake detection. In addition to fake detection, the proposed framework provides textual explanations for its decisions. The results obtained using the Presidential Deepfakes Dataset and World Leaders Dataset of real and manipulated videos demonstrate the effectiveness of our approach in detecting deepfakes and highlight the potential of a neurosymbolic approach for expandability.<\/jats:p>","DOI":"10.1145\/3624748","type":"journal-article","created":{"date-parts":[[2023,9,20]],"date-time":"2023-09-20T11:27:23Z","timestamp":1695209243000},"page":"1-16","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":25,"title":["Multimodal Neurosymbolic Approach for Explainable Deepfake Detection"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8201-7372","authenticated-orcid":false,"given":"Ijaz Ul","family":"Haq","sequence":"first","affiliation":[{"name":"Department of\u00a0Computer Science and\u00a0Engineering, Oakland University, Rochester, MI, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7927-3436","authenticated-orcid":false,"given":"Khalid Mahmood","family":"Malik","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Oakland University, Rochester, MI, USA and College of Innovation and Technology, The University of Michigan-Flint, Flint, MI, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5302-1150","authenticated-orcid":false,"given":"Khan","family":"Muhammad","sequence":"additional","affiliation":[{"name":"Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab), Department of Applied Artificial Intelligence, School of Convergence, College of Computing and Informatics, Sungkyunkwan University, Jongno-gu, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,9,12]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2022.3221811"},{"issue":"1","key":"e_1_3_1_3_2","first-page":"1","article-title":"Sensing e-banking cybercrimes vulnerabilities via smart information sciences strategies","volume":"1","author":"Al-Shaarani F.","year":"2020","unstructured":"F. 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