Exploiting Human Social Cognition for the Detection of Fake and Fraudulent Faces via Memory Networks
This addresses the critical issue of disinformation from synthetic media for governments and society, offering a more generalizable detection framework.
The paper tackles the problem of detecting fake and fraudulent faces that generalize across unseen manipulation types, proposing a Hierarchical Memory Network (HMN) that achieves superior performance compared to state-of-the-art methods.
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 Memory Network (HMN) architecture, which is able to successfully detect faked faces by utilising knowledge stored in neural memories as well as visual cues to reason about the perceived face and anticipate its 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 compared to the state-of-the-art.