{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T13:08:57Z","timestamp":1782997737638,"version":"3.54.5"},"reference-count":47,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T00:00:00Z","timestamp":1739923200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2025,3,31]]},"abstract":"<jats:p>\n            Deepfake technology presents critical cybersecurity challenges that have become more popular since easily accessible applications have become more widely available. The proliferation of fake portrait videos constitutes a serious risk to the legal system, society, and personal privacy. The publication of fraudulent explicit content starring celebrities, the circulation of fake political videos, and the use of faked impersonated videos as proof in court of law are all examples of the effects of deepfakes in the real world. In reaction to this growing threat, we propose a\n            <jats:italic>simple<\/jats:italic>\n            yet\n            <jats:italic>efficient<\/jats:italic>\n            Person of Interest (PoI) Siamese network-based model to detect deepfake synthetic content in portrait images, providing a preventative measure against the growing danger of deepfakes. On one side and unlike traditional neural networks, which process inputs independently, our approach leverages a Siamese network that processes two inputs simultaneously using identical sub-networks with shared weights and parameters. This twin structure is particularly effective for our adopted PoI methodology, where one input is a reference image of a specific individual, and the other is an image that needs to be verified as either real or fake. By ensuring that both the reference and the suspect image are processed in the same way, the network can accurately learn and detect subtle differences, enabling it to determine whether the second image is a genuine representation of the individual or a deepfake. This makes our method particularly relevant for targeted forensic investigations or security applications where an individual\u2019s media integrity is paramount. On the other side, our proposed method does not require any additional complex biological feature extraction. Despite this simplification, our method achieves comparable accuracy to more complex models that rely on biological feature extraction. This efficiency makes our approach practical for implementation on resource-constrained devices, such as mobile phones and Internet of Things (IoT) systems.\n          <\/jats:p>","DOI":"10.1145\/3708352","type":"journal-article","created":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T14:08:37Z","timestamp":1734617317000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Siamese Network-Based Detection of Deepfake Impersonation Attacks with a Person of Interest Approach"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2696-2024","authenticated-orcid":false,"given":"Khouloud","family":"Samrouth","sequence":"first","affiliation":[{"name":"Arab Open University\u2013Lebanon, Beirut, Lebanon and Faculty of Engineering, LebaneseUniversity, Beirut, Lebanon"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-0600-271X","authenticated-orcid":false,"given":"Pia","family":"El Housseini","sequence":"additional","affiliation":[{"name":"Arab Open University\u2013Lebanon, Beirut, Lebanon"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0750-0959","authenticated-orcid":false,"given":"Olivier","family":"Deforges","sequence":"additional","affiliation":[{"name":"Univ Rennes, INSA Rennes, CNRS, IETR (UMR 6164), Rennes, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,2,19]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"[n. d.]. Internet Crime Complaint Center (IC3)\u2014Deepfakes and stolen PII utilized to apply for remote work positions. 2022. Retrieved December 09 2023 from https:\/\/www.ic3.gov\/Media\/Y2022\/PSA220628"},{"key":"e_1_3_1_3_2","unstructured":"[n. d.]. Siamese Network-Based Detection of DeepFake Impersonation Attacks. Retrieved from https:\/\/github.com\/ksamrouth\/Siamese-Network-Based-Detection-of-DeepFake-Impersonation-Attacks\/tree\/main"},{"key":"e_1_3_1_4_2","first-page":"981","volume-title":"IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Agarwal Shruti","year":"2021","unstructured":"Shruti Agarwal and Hany Farid. 2021. Detecting deep-fake videos from aural and oral dynamics. 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