Deepfake Detection with Metaheuristic Algorithms and Deep Features
INFORMATION TECHNOLOGY AND CONTROL, cilt.54, sa.4, 2025 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 54 Sayı: 4
- Basım Tarihi: 2025
- Doi Numarası: 10.5755/j01.itc.54.4.40518
- Dergi Adı: INFORMATION TECHNOLOGY AND CONTROL
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Directory of Open Access Journals
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- Gazi Üniversitesi Adresli: Evet
Özet
The rapid development and spread of deepfake technology have posed serious threats to cybersecuri-ty, information security, privacy, and public safety; consequently, reliable detection of deepfake content has become a critical necessity. We present a metaheuristic-based hybrid approach that combines deep learning architectures with metaheuristic algorithms. In the study conducted on FaceForensics (FF++) and Celeb-DF datasets, four feature vectors extracted from Xception and ResNet50 architectures under-went a metaheuristic-based feature selection process incorporating Cuckoo Search Algorithm (CS), Suc-cess-History Based Adaptive Differential Evolution with Linear Population Size Reduction (L-SHADE), Particle Swarm Optimization (PSO) and Weighted Mean of Vectors Optimization Algorithm (INFO) algo-rithms. Sub-feature vectors were obtained through feature selection for each algorithm. The four feature vectors obtained from the architectures and sixteen sub-feature vectors generated after feature selection were classified using machine learning and deep learning methods. When comparing performance met-rics before and after feature selection, the INFO algorithm provided the highest performance across both datasets, achieving AUC values of 99.05% for the FF++ dataset and 99.01% for the Celeb-DF dataset. We believe that our comprehensive experimental study demonstrates more significant and effective results than existing methods