论文标题
学习掩盖：朝向广义伪造检测
Learning to mask: Towards generalized face forgery detection
论文作者
论文摘要
不看到伪造类型的普遍性对于面部伪造探测器至关重要。最近的工作通过合成伪造数据增强在概括方面取得了重大进展。在这项工作中，我们探索了改善概括的另一个途径。我们的目标是减少在训练阶段易于学习的功能，以降低过度适合特定伪造类型的风险。具体而言，在我们的方法中，教师网络将面部图像输入，并通过各种多头关注的Vit生成深层特征的注意力图。注意力图用于指导学生网络通过降低高度参与的深度功能来关注低级功能。还提出了一种深度特征混合策略，以合成特征域中的伪造。实验表明，没有数据增强，我们的方法能够在看不见的伪造和高度压缩的数据上实现有希望的性能。
Generalizability to unseen forgery types is crucial for face forgery detectors. Recent works have made significant progress in terms of generalization by synthetic forgery data augmentation. In this work, we explore another path for improving the generalization. Our goal is to reduce the features that are easy to learn in the training phase, so as to reduce the risk of overfitting on specific forgery types. Specifically, in our method, a teacher network takes as input the face images and generates an attention map of the deep features by a diverse multihead attention ViT. The attention map is used to guide a student network to focus on the low-attended features by reducing the highly-attended deep features. A deep feature mixup strategy is also proposed to synthesize forgeries in the feature domain. Experiments demonstrate that, without data augmentation, our method is able to achieve promising performances on unseen forgeries and highly compressed data.
