{"ID":2841211,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.12048","arxiv_id":"2511.12048","title":"DeiTFake: Deepfake Detection Model using DeiT Multi-Stage Training","abstract":"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.","short_abstract":"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 us...","url_abs":"https://arxiv.org/abs/2511.12048","url_pdf":"https://arxiv.org/pdf/2511.12048v1","authors":"[\"Saksham Kumar\",\"Ashish Singh\",\"Srinivasarao Thota\",\"Sunil Kumar Singh\",\"Chandan Kumar\"]","published":"2025-11-15T05:55:09Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.CR\"]","methods":"[\"Transformer\"]","has_code":false}