Detecting Deep Fake Images using Deep Learning and Generative Adversarial Networks

Deepfake technology has become a significant threat due to its ability to allow people to create highly realistic synthetic media without any high computational cost. This may cause many troubles to the existing system by spreading misinformation, violating an individual’s privacy, and pose a threat to digital security. The proposed model is a combination of XceptionNet, Support Vector Machines(SVM) and Auto Encoded Generative Adversarial Networks (AGAN) which accurately identify the visual anomalies such as pixel inconsistencies, differences in the lighting and facial distortions embedded in the deepfakes. Xception Net is included for its exceptional feature extraction capabilities, capturing the slightest details in the images often exploited in deepfakes. SVM is included for its robust classification performance which helps in differentiating between real and fake images accurately based on extracted features. AGAN improves the detection process by focusing on attention mechanisms that features suspicious regions in the image. It also improves the model’s ability to detect subtle forgeries and inconsistencies. The approach is tested rigorously on a data set consisting of both real and tampered images, demonstrating high accuracy and robustness in deepfake detection. This project contributes to the development of dependable tools for analyzing and detecting deepfake images, addressing the growing challenges posed by the digital security and information manipulation.