Deepfakes are major threats to the integrity of digital media. We propose DeiTFake, a DeiT-based transformer and a novel two-stage progressive training strategy with increasing augmentation complexity. The approach applies an initial transfer-learning phase with standard augmentations followed by a fine-tuning phase using advanced affine and deepfake-specific augmentations. DeiT's knowledge distillation model captures subtle manipulation artifacts, increasing robustness of the detection model. Trained on the OpenForensics dataset (190,335 images), DeiTFake achieves 98.71\% accuracy after stage one and 99.22\% accuracy with an AUROC of 0.9997, after stage two, outperforming the latest OpenForensics baselines. We analyze augmentation impact and training schedules, and provide practical benchmarks for facial deepfake detection.
@article{arxiv.2511.12048,
title = {DeiTFake: Deepfake Detection Model using DeiT Multi-Stage Training},
author = {Saksham Kumar and Ashish Singh and Srinivasarao Thota and Sunil Kumar Singh and Chandan Kumar},
journal= {arXiv preprint arXiv:2511.12048},
year = {2026}
}