论文标题
聚集层以进行深层检测
Aggregating Layers for Deepfake Detection
论文作者
论文摘要
面部操纵（深摄影）和合成面部创造的日益普及提出了开发强大的伪造检测解决方案的必要性。至关重要的是，该域中的大多数工作都假定测试集中的深击来自用于训练网络的相同深击算法。这不是实践中的事情。取而代之的是，我们考虑了网络在一种深泡算法上训练的情况，并对另一种算法产生的深击进行了测试。通常，有监督的技术遵循从深backbon中提取视觉特征的管道，然后是二进制分类头。取而代之的是，我们的算法聚合功能在一个骨干网络的所有层中提取以检测一个假货。我们评估了我们对两个感兴趣领域的方法 - 深膜检测和合成图像检测，并发现我们达到了SOTA结果。
The increasing popularity of facial manipulation (Deepfakes) and synthetic face creation raises the need to develop robust forgery detection solutions. Crucially, most work in this domain assume that the Deepfakes in the test set come from the same Deepfake algorithms that were used for training the network. This is not how things work in practice. Instead, we consider the case where the network is trained on one Deepfake algorithm, and tested on Deepfakes generated by another algorithm. Typically, supervised techniques follow a pipeline of visual feature extraction from a deep backbone, followed by a binary classification head. Instead, our algorithm aggregates features extracted across all layers of one backbone network to detect a fake. We evaluate our approach on two domains of interest - Deepfake detection and Synthetic image detection, and find that we achieve SOTA results.
