Deepfake Image Detection

The ease with which generative AI models are improving today makes it rather difficult to distinguish between actual images and those that are forged/made using AI. However, with the growing usage of deepfake multimedia content in social, entertainment, and political setups, the concern for misinformation, identity theft, and reputation damage is increasing at a rapid pace. To address this concern, the current research aims at detecting forged images using deep learning and checks the efficiency of efficient convolution models. In this process, the research considers both XceptionNet and EfficientB0 models enhanced with Dense layers, Dropout, Batch Normalization, and adaptive learning rate approaches. Simultaneously, facial region extraction/preprocessing tasks are handled through OpenCV, NumPy, and MTCNN, as needed, to allow the models to detect the subtle details created during the forged image generation process. The models are trained on a diversified data-set assembled from various benchmark datasets to make them robust to varying resolutions, compression, as well as various manipulation techniques. The performance analysis with reference to accuracy, precision, recall, F1-score, confusion matrix, and receiver operating characteristic curves indicates that a very reliable approach with a maximum accuracy of 99.06% can be obtained with the suggested pipeline. To cater to real-time requirements, a Flask API with webcam support can also be incorporated into the system. In conclusion, the importance of deep architectures in detecting counterfeit images has been established in this research, and it will help in improving the area of digital media forensics. This research has numerous applications in the field of digital media.