Deep Fake Image Detection Using Deep Learning

Deep fake images trigger doubts regarding media trustworthiness as well as they compromise public confidence and create security threats. A new deep fake detection system that uses EfficientNetV2 serves as an efficient method for this task. The choice of EfficientNetV2 stems from its improved feature extraction capabilities and its capability to compute quickly as well as identify minor artifacts in facial features and textures and light changes. Extensive testing of the methodology occurs as we examine Accuracy and Precision and Recall with a geometric model on actual image datasets and modified versions of these images. Effective preprocessing and an innovative architecture enhance detection precision, enabling real-time application. The method reinforces digital forensics investigations which helps both ethical and security-focused institutions.