Deep Learning-Based Facial Deepfake Detection Using MobileNetV2 and VGG16

Deep learning models are being used widely in the world, and this has led to major developments and advancements in many computer vision applications, including face identification. Other than that, there is rising worry over the production and distribution of fake faces. These are produced by generative models due to the possible misuse of technology and increasing generative media contents. Using deep learning techniques, specifically MobileNetV2 and VGG16, this work completes the critical requirement for fake face identification. The combination of these two deep learning architectures improves the model’s ability to distinguish between real and fake face photographs. The deeper layers of VGG16 help to capture complex patterns suggestive of face originality, while the lightweight structure of MobileNetV2 facilitates effective feature extraction. By combining the advantages of the two models, the hybrid technique creates a reliable and precise fake face detection system. Results from experiments reveal that the suggested approach is successful in differentiating between real and fake faces, which fights against the growing threat of fake images in a variety of fields, including social media and authentication systems.