通过多阶段训练提升深度伪造检测能力，显著改善模型对伪造痕迹的识别。
Herd Mentality in Augmentation -- Not a Good Idea! A Robust Multi-stage Approach towards Deepfake Detection
- 设计多阶段架构，融合加权损失与掩码眼部预训练，增强对伪造痕迹的关注。
- 在Celeb-DF v2数据集上，准确率提升4.34%，F1分数提高1.71%。
- 适合关注深度伪造检测、对抗性攻击防御的研究者和安全应用开发者。
深度伪造技术的快速发展引发了对数字媒体真实性的严重担忧。准确检测深度伪造对保障数字内容安全至关重要。然而，大多数标准图像分类器难以区分真实与伪造人脸。我们的分析表明，这一失败源于模型无法显式聚焦于深度伪造中常见的伪影特征。为此，我们基于GenConViT模型提出一种增强架构，引入加权损失、更新增强技术及掩码眼部预训练。该模型在Celeb-DF v2数据集上实现准确率提升4.34%，F1分数提高1.71%。代码已开源：https://github.com/Monu-Khicher-1/multi-stage-learning。
原文摘要 · Abstract (English)
The rapid increase in deepfake technology has raised significant concerns about digital media integrity. Detecting deepfakes is crucial for safeguarding digital media. However, most standard image classifiers fail to distinguish between fake and real faces. Our analysis reveals that this failure is due to the model's inability to explicitly focus on the artefacts typically in deepfakes. We propose an enhanced architecture based on the GenConViT model, which incorporates weighted loss and update augmentation techniques and includes masked eye pretraining. This proposed model improves the F1 score by 1.71% and the accuracy by 4.34% on the Celeb-DF v2 dataset. The source code for our model is available at https://github.com/Monu-Khicher-1/multi-stage-learning
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