DeiTFake: Deepfake Detection Model using DeiT Multi-Stage Training

TL;DR

DeiTFake introduces a two-stage training strategy for a DeiT-based transformer that significantly improves deepfake detection accuracy by leveraging progressive augmentation techniques and knowledge distillation.

Contribution

The paper presents a novel two-stage training approach with advanced augmentations for DeiT-based deepfake detection, achieving state-of-the-art accuracy on the OpenForensics dataset.

Findings

Achieves 99.22% accuracy and 0.9997 AUROC after training.

Two-stage training with progressive augmentation enhances robustness.

Outperforms existing deepfake detection baselines.

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.