Deepfake Image Detection using Deep Learning

The proliferation of AI-driven image manipulation techniques has positioned deepfake images as a notable threat to digital authenticity and public trust. This project centers on the implementation phase of a detection system aimed at identifying deepfake images through deep learning methodologies. A structured pipeline was established, commencing with the curation and preprocessing of datasets utilizing publicly available deepfake collections, such as FaceForensics++ and Celeb-DF. To enhance the robustness of the model, images underwent standardization processes including face alignment, cropping, and augmentation. In the evaluation phase, various advanced architectures were analyzed, encompassing Convolutional Neural Networks (CNNs) and transfer learning models such as XceptionNet, for the binary classification of genuine and fabricated images. The system’s training and fine-tuning involved the use of optimized hyperparameters and regularization techniques to mitigate the risk of overfitting. The assessment of performance was conducted through the application of metrics including accuracy, precision, recall, F1-score, and AUC-ROC. The results of the experiments indicate a high level of detection capacity, as the model attained significant accuracy on previously untested data and effectively generalized across various forms of manipulations. This implementation illustrates both the advantages and obstacles associated with the deployment of deepfake detection tools in practical scenarios. These challenges encompass concerns regarding adversarial robustness and the ability to generalize across diverse datasets.