Enhancing Deepfake Detection: An Ensemble Deep Learning Approach for Efficient Attribute Manipulation Identification

The increasing use of deepfake technology by incorporating Artificial Intelligence (AI) to seamlessly replace faces in videos, poses a significant threat to individuals, societies, and national security. This research study addresses this growing concern by detecting deepfake classification with the integration of two powerful Convolutional Neural Network (CNN) models: InceptionV3 and EfficientNetB0. The existing deepfake detection systems predominantly rely on facial feature analysis, analyzing subtle inconsistencies; however, these methods are susceptible to evolving deepfake techniques. In response, the proposed ensemble model exploits the advantages of InceptionV3 and EfficientNetB0 models to capture intricate features and computational efficiency. The synergy between these models significantly enhances the accuracy upto 93% and adaptability of the proposed deepfake detection system. When compared with conventional facial feature analysis, this approach establishes a resilient defense against emerging deepfake threats. As deepfake technology continues to advance, necessitating continual research in face-based detection systems, this study proposes a cutting-edge ensemble approach that not only mitigates the risks associated with social media manipulation but also serves as a proactive measure against potential challenges in future.