An Applied Transfer Learning Framework for Deepfake Image and Video Detection

Generative adversarial networks and diffusionbased synthesis have made photo-realistic facial manipulation — commonly called deepfakes — inexpensive, fast and widely accessible. Such synthetic media is increasingly used for misinformation, impersonation, financial fraud and reputational attack, and human observers perform poorly at distinguishing it from authentic content. Detection methods reported in the literature achieve high accuracy when training and test manipulations originate from the same generator, but degrade sharply on unseen generation techniques, and most are released as research code rather than as usable end-to-end systems. This paper presents DeepGuard, an applied detection framework that couples MTCNN-based face detection and alignment, intervalbased frame sampling for video input, and an ImageNetpretrained XceptionNet backbone fine-tuned by two-stage transfer learning, into a single pipeline that classifies an uploaded image or video as real or deepfake and returns a confidence score. The contribution is not a new network topology but an engineered, reproducible pipeline together with a controlled comparison of convolutional backbones under one fixed preprocessing and videodisjoint splitting protocol. The system was evaluated on the Face Forensics++ (c23) corpus using a 70/15/15 split partitioned by source video, and achieved 92.40% accuracy, 93.10% precision, 91.80% recall, an F1-score of 0.9244 and a ROC-AUC of 0.9612 on the held-out test set. Limitations concerning cross-generator generalisation, compression robustness and audio-driven forgeries are analysed, and directions for extending the framework are outlined.