论文标题
通过动态原型可解释且值得信赖的深击检测
Interpretable and Trustworthy Deepfake Detection via Dynamic Prototypes
论文作者
论文摘要
在本文中，我们提出了一种以人类为中心的新方法来检测面部图像中的伪造方法，使用动态原型作为视觉解释的一种形式。当前，大多数最先进的深层检测是基于黑框模型，该模型处理逐帧的视频进行推理，很少有几乎可以仔细检查其时间不一致。但是，在深击视频中存在这种暂时的人工制品是对监督人的检测和解释深层效果的关键。为此，我们提出了动态原型网络（DPNET） - 一种可解释有效的解决方案，利用动态表示（即原型）来解释深层时间伪像。广泛的实验结果表明，即使在Google的DeepFakedEtection，DeepererForensics和Celeb-DF等看不见的测试数据集中，DPNET也可以实现竞争性的预测性能，同时提供了对DeepFake Dynamics的简单引用解释。除了DPNET的原型框架之外，我们还基于这些动态进一步制定时间逻辑规格，以检查模型对所需的时间行为的遵守，因此为这种关键检测系统提供了可信度。
In this paper we propose a novel human-centered approach for detecting forgery in face images, using dynamic prototypes as a form of visual explanations. Currently, most state-of-the-art deepfake detections are based on black-box models that process videos frame-by-frame for inference, and few closely examine their temporal inconsistencies. However, the existence of such temporal artifacts within deepfake videos is key in detecting and explaining deepfakes to a supervising human. To this end, we propose Dynamic Prototype Network (DPNet) -- an interpretable and effective solution that utilizes dynamic representations (i.e., prototypes) to explain deepfake temporal artifacts. Extensive experimental results show that DPNet achieves competitive predictive performance, even on unseen testing datasets such as Google's DeepFakeDetection, DeeperForensics, and Celeb-DF, while providing easy referential explanations of deepfake dynamics. On top of DPNet's prototypical framework, we further formulate temporal logic specifications based on these dynamics to check our model's compliance to desired temporal behaviors, hence providing trustworthiness for such critical detection systems.
