Convolutional neural network based face forgery detection methods have achieved remarkable results during training, but struggled to maintain comparable performance during testing. We observe that the detector is prone to focus more on content information than artifact traces, suggesting that the detector is sensitive to the intrinsic bias of the dataset, which leads to severe overfitting. Motivated by this key observation, we design an easily embeddable disentanglement framework for content information removal, and further propose a Content Consistency Constraint (C2C) and a Global Representation Contrastive Constraint (GRCC) to enhance the independence of disentangled features. Furthermore, we cleverly construct two unbalanced datasets to investigate the impact of the content bias. Extensive visualizations and experiments demonstrate that our framework can not only ignore the interference of content information, but also guide the detector to mine suspicious artifact traces and achieve competitive performance.
翻译:在培训期间,基于以伪造物为主的进化神经网络表面检测方法取得了显著成果,但在测试期间努力保持类似的性能。我们观察到,探测器比文物痕迹更倾向于关注内容信息,这表明探测器对数据集的内在偏差敏感,这导致严重过度配置。我们受这一关键观察的驱动,设计了一个易于嵌入的消除内容信息分解框架,并进一步提议了内容一致性约束(C2C)和全球代表制约束(GRCC),以加强分解特征的独立性。此外,我们巧妙地构建了两个不平衡的数据集,以调查内容偏差的影响。广泛的视觉化和实验表明,我们的框架不仅可以忽视内容信息的干扰,而且还可以指导探测器销毁可疑的文物痕迹并实现竞争性性能。