TY - EJOU AU - Raza, Ahmad AU - Basit, Abdul AU - Ahmed, Syed Muqtar AU - Arfeen, Zeeshan Ahmad AU - Masud, Muhammad I. AU - Zamir, Muhammad Farid AU - Azam, Mehreen Kausar AU - Jumani, Touqeer Ahmed TI - Vision Transformer–Based Deepfake Detection Across Multiple Generation Methods: A Transfer Learning Approach T2 - Computers, Materials \& Continua PY - 2026 VL - 89 IS - 2 SN - 1546-2226 AB - The development of deepfake technologies is a threat to digital media authentication and cybersecurity infrastructure. The current paper proposes a method for detecting manipulated images of faces based on the Vision Transformer architecture. We fine-tune a pre-trained ViT-Base-Patch16-224 model based on this well-curated dataset of 12,137 face images, which includes an almost equal number of real and synthetic face images using a variety of different generation methods. The data set contains real-life photographs of CelebA and FFHQ, along with artificial samples of the publicly available Kaggle repositories (FaceForensics++, Celeb-DF, and DFDC) and 600 self-collected photos (300 real-life photographs of personal cell phones and 300 artificial ones created with the help of modern tools) to make it closer to real-life use. Methodology involves assessment of the quality of data, systematic preprocessing by ImageNet normalization, data augmentation, and stratification of a 70-20-10 partitioning of the data. AdamW optimization using a learning rate of 2 × 10–5 was used with 8 epochs. The test accuracy of the system was 99.01%, the precision was 98.85%, and the recall was 99.18%, with 12 misclassifications. The results demonstrate that Vision Transformers can effectively model global image dependencies for detecting Deepfakes. The complete methodology is documented in this paper to ensure reproducibility. KW - Deep learning; deepfake detection; digital forensics; facial manipulation; image authentication; machine learning; vision transformer DO - 10.32604/cmc.2026.084902