Advances in computer vision have brought us to the point where we have the ability to synthesise realistic fake content. Such approaches are seen as a source of disinformation and mistrust, and pose serious concerns to governments around the world. Convolutional Neural Networks (CNNs) demonstrate encouraging results when detecting fake images that arise from the specific type of manipulation they are trained on. However, this success has not transitioned to unseen manipulation types, resulting in a significant gap in the line-of-defense. We propose a Hierarchical Attention Memory Network (HAMN), motivated by the social cognition processes of the human brain, for the detection of fake faces. Through visual cues and by utilising knowledge stored in neural memories, we allow the network to reason about the perceived face and anticipate it’s future semantic embeddings. This renders a generalisable face tampering detection framework. Experimental results demonstrate the proposed approach achieves superior performance for fake and fraudulent face detection.

中文翻译：

---

通过神经记忆网络检测虚假和欺诈性面孔


计算机视觉的进步使我们有能力合成真实的虚假内容。此类做法被视为虚假信息和不信任的根源，并引起世界各国政府的严重关切。卷积神经网络 (CNN) 在检测由于训练的特定类型操作而产生的虚假图像时，表现出了令人鼓舞的结果。然而，这种成功并没有转变为看不见的操纵类型，导致防线出现显着缺口。我们提出了一种受人脑社会认知过程启发的分层注意力记忆网络（HAMN），用于检测假脸。通过视觉线索并利用神经记忆中存储的知识，我们允许网络推理感知到的面部并预测其未来的语义嵌入。这提供了一个通用的面部篡改检测框架。实验结果表明，所提出的方法在虚假和欺诈性人脸检测方面取得了优异的性能。