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Advances in computer vision have brought us to the point where we have theability to synthesise realistic fake content. Such approaches are seen as asource of disinformation and mistrust, and pose serious concerns to governmentsaround the world. Convolutional Neural Networks (CNNs) demonstrate encouragingresults when detecting fake images that arise from the specific type ofmanipulation they are trained on. However, this success has not transitioned tounseen manipulation types, resulting in a significant gap in theline-of-defense. We propose a Hierarchical Memory Network (HMN) architecture,which is able to successfully detect faked faces by utilising knowledge storedin neural memories as well as visual cues to reason about the perceived faceand anticipate its future semantic embeddings. This renders a generalisableface tampering detection framework. Experimental results demonstrate theproposed approach achieves superior performance for fake and fraudulent facedetection compared to the state-of-the-art.

Exploiting Human Social Cognition for the Detection of Fake and Fraudulent Faces via Memory Networks