DeiTFake: Deepfake Detection Model using DeiT Multi-Stage Training
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915836400238592 |
|---|---|
| author | Kumar, Saksham Singh, Ashish Thota, Srinivasarao Singh, Sunil Kumar Kumar, Chandan |
| author_facet | Kumar, Saksham Singh, Ashish Thota, Srinivasarao Singh, Sunil Kumar Kumar, Chandan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_12048 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DeiTFake: Deepfake Detection Model using DeiT Multi-Stage Training Kumar, Saksham Singh, Ashish Thota, Srinivasarao Singh, Sunil Kumar Kumar, Chandan Computer Vision and Pattern Recognition Cryptography and Security 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. |
| title | DeiTFake: Deepfake Detection Model using DeiT Multi-Stage Training |
| topic | Computer Vision and Pattern Recognition Cryptography and Security |
| url | https://arxiv.org/abs/2511.12048 |