用分阶段增强训练提升深度伪造检测准确率
DeiTFake: Deepfake Detection Model using DeiT Multi-Stage Training
- 分两阶段训练：先标准增强，再用高级仿射和伪造专用增强
- 在OpenForensics数据集上达99.22%准确率，AUROC接近1
- 适合需要高精度伪造检测的安防与媒体审核场景
深度伪造对数字媒体真实性构成重大威胁。本文提出DeiTFake，一种基于DeiT的Transformer模型及新颖的两阶段渐进式训练策略，逐步增加数据增强复杂度。首先进行标准增强的迁移学习，随后采用先进的仿射变换和深度伪造特有增强进行微调。利用DeiT的知识蒸馏机制捕捉细微篡改痕迹，提升检测模型鲁棒性。在包含190,335张图像的OpenForensics数据集上，第一阶段达到98.71%准确率，第二阶段进一步提升至99.22%，AUROC达0.9997，优于现有最新基线。论文分析了增强策略影响与训练调度，为面部深度伪造检测提供了实用基准。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。