# DeiTFake: Deepfake Detection Model using DeiT Multi-Stage Training Source: [DeiTFake: Deepfake Detection Model using DeiT Multi-Stage Training]() Collection index: [llms.txt]() Data updated: 2026-03-07T05:00:10.880Z Authors: Saksham Kumar, Ashish Singh, Srinivasarao Thota, Sunil Kumar Singh, Chandan Kumar Published: 2025-11-15T05:55:09.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Deepfake detection on OpenForensics (test)](): Deepfake Detection; OpenForensics (test); Accuracy: 99.22. ## Original sources and implementation [Original paper]() [PDF]()