Hybrid AHA-PLO Metaheuristic Feature Selection for Robust Deepfake Video Detection

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.