Hybrid AHA-PLO Metaheuristic Feature Selection for Robust Deepfake Video Detection
APPLIED SCIENCES-BASEL, cilt.15, sa.21, 2025 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 15 Sayı: 21
- Basım Tarihi: 2025
- Doi Numarası: 10.3390/app152111716
- Dergi Adı: APPLIED SCIENCES-BASEL
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Directory of Open Access Journals
- Gazi Üniversitesi Adresli: Evet
Özet
The widespread use of deepfake technologies has increased the demand for accurate and effective detection methods. This study presents a novel deepfake detection framework that utilizes meta-heuristic feature selection to enhance classification performance. The performance of the Artificial Hummingbird Algorithm (AHA), Polar Lights Optimization (PLO), and their hybrid model, AHA-PLO, is investigated. The hybrid model aims to conduct a more effective search in the feature space by combining AHA's global exploration ability with PLO's local exploitation precision. Experimental evaluations conducted on two benchmark datasets, FaceForensics++ (FF++) and Celeb-DF (CDF), demonstrate that the proposed AHA-PLO model consistently outperforms its individual components, achieving state-of-the-art AUC scores of 99.36% on FF++ and 98.78% on CDF. These findings support the hybrid model's potential as a robust and generalizable solution for deepfake video detection.