{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,9]],"date-time":"2026-08-09T10:03:23Z","timestamp":1786269803752,"version":"3.56.0"},"reference-count":34,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,4,15]],"date-time":"2024-04-15T00:00:00Z","timestamp":1713139200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deanship of Scientific Research at Al-Balqa Applied University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Deepfakes are digital audio, video, or images manipulated using machine learning algorithms. These manipulated media files can convincingly depict individuals doing or saying things they never actually did. Deepfakes pose significant risks to our lives, including national security, financial markets, and personal privacy. The ability to create convincing deep fakes can also harm individuals\u2019 reputations and can be used to spread disinformation and fake news. As such, there is a growing need for reliable and accurate methods to detect deep fakes and prevent their harmful effects. In this paper, a hybrid convolutional neural network (CNN) and recurrent neural network (RNN) with a particle swarm optimization (PSO) algorithm is utilized to demonstrate a deep learning strategy for detecting deepfake videos. High accuracy, sensitivity, specificity, and F1 score were attained by the proposed approach when tested on two publicly available datasets: Celeb-DF and the Deepfake Detection Challenge Dataset (DFDC). Specifically, the proposed method achieved an average accuracy of 97.26% on Celeb-DF and an average accuracy of 94.2% on DFDC. The results were compared to other state-of-the-art methods and showed that the proposed method outperformed many. The proposed method can effectively detect deepfake videos, which is essential for identifying and preventing the spread of manipulated content online.<\/jats:p>","DOI":"10.3390\/computers13040099","type":"journal-article","created":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T09:16:33Z","timestamp":1713258993000},"page":"99","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Detection of Deepfake Media Using a Hybrid CNN\u2013RNN Model and Particle Swarm Optimization (PSO) Algorithm"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1503-118X","authenticated-orcid":false,"given":"Aryaf","family":"Al-Adwan","sequence":"first","affiliation":[{"name":"Department of Autonomous Systems, Al-Balqa Applied University, Al-Salt 19117, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6768-9696","authenticated-orcid":false,"given":"Hadeel","family":"Alazzam","sequence":"additional","affiliation":[{"name":"Department of Intelligent Systems, Al-Balqa Applied University, Al-Salt 19117, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Noor","family":"Al-Anbaki","sequence":"additional","affiliation":[{"name":"King Abdullah II School for Information Technology, University of Jordan, Amman 11942, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eman","family":"Alduweib","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Petra, Amman 11196, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,15]]},"reference":[{"key":"ref_1","first-page":"2250","article-title":"Deepfake: A survey","volume":"23","author":"Zhou","year":"2021","journal-title":"IEEE Trans. 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