An Approach to Deepfake Video Detection Based on ACO-PSO Features and Deep Learning

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dc.authoridCELIK, YUKSEL/0000-0002-7117-9736
dc.contributor.authorAlhaji, Hanan Saleh
dc.contributor.authorCelik, Yuksel
dc.contributor.authorGoel, Sanjay
dc.date.accessioned2024-09-29T16:08:05Z
dc.date.available2024-09-29T16:08:05Z
dc.date.issued2024
dc.departmentKarabük Üniversitesien_US
dc.description.abstractThe rapid advancement of deepfake technology presents significant challenges in detecting highly convincing fake videos, posing risks such as misinformation, identity theft, and privacy violations. In response, this paper proposes an innovative approach to deepfake video detection by integrating features derived from ant colony optimization-particle swarm optimization (ACO-PSO) and deep learning techniques. The proposed methodology leverages ACO-PSO features and deep learning models to enhance detection accuracy and robustness. Features from ACO-PSO are extracted from the spatial and temporal characteristics of video frames, capturing subtle patterns indicative of deepfake manipulation. These features are then used to train a deep learning classifier to automatically distinguish between authentic and deepfake videos. Extensive experiments using comparative datasets demonstrate the superiority of the proposed method in terms of detection accuracy, robustness to manipulation techniques, and generalization to unseen data. The computational efficiency of the approach is also analyzed, highlighting its practical feasibility for real-time applications. The findings revealed that the proposed method achieved an accuracy of 98.91% and an F1 score of 99.12%, indicating remarkable success in deepfake detection. The integration of ACO-PSO features and deep learning enables comprehensive analysis, bolstering precision and resilience in detecting deepfake content. This approach addresses the challenges involved in facial forgery detection and contributes to safeguarding digital media integrity amid misinformation and manipulation.en_US
dc.identifier.doi10.3390/electronics13122398
dc.identifier.issn2079-9292
dc.identifier.issue12en_US
dc.identifier.scopus2-s2.0-85197243804en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.3390/electronics13122398
dc.identifier.urihttps://hdl.handle.net/20.500.14619/7351
dc.identifier.volume13en_US
dc.identifier.wosWOS:001256664200001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofElectronicsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectdeepfake detectionen_US
dc.subjectdeep learningen_US
dc.subjectant colony optimizationen_US
dc.subjectparticle swarm optimizationen_US
dc.titleAn Approach to Deepfake Video Detection Based on ACO-PSO Features and Deep Learningen_US
dc.typeArticleen_US

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