Hybrid deep feature integration model for robust deepfake detection using transfer-learned neural networks

Experiments supervised on Datasets of Celeb- DF(v2) and Kaggle exhibit that the proposed hybrid approach enhances macro- F1, calibration error, and temporal robustness compared to baseline models. The proposed model obtains a competitive outcome under constrained computational resources, making it appropriate for forensic applications, real-world media authentication systems, low-power deployments, and scalable deepfake screening pipelines.