Enhanced Deep Fake Image Detection via Feature Fusion of EfficientNet, Xception, and ResNet Models

In recent years, the rapid advancements in deep learning have led to the creation of realistic fake images, commonly referred to as deep fakes. Detecting these images has emerged as a critical challenge in the fields of cyber-security, digital forensics, and privacy protection. This paper proposes a novel deep fake detection model that utilizes feature fusion from three powerful pre-trained convolutional neural networks (CNNs): EfficientNetB0, Xception, and ResNet50. By leveraging the strengths of each model, discriminative features are extracted to enhance classification accuracy. The approach combines features from multiple networks, which are then processed through dense layers to classify real and fake images. Evaluated on a dataset of real and fake faces, the proposed model demonstrates significant improvements in detection accuracy and generalization compared to traditional single-model approaches. Comprehensive performance metrics, including precision, recall, and F1-score, are reported, highlighting that the ensemble model outperforms conventional CNN models in deep fake detection.