PRNU-based Deepfake Detection
Florian Lugstein, Simon Baier, Gregor Bachinger, Andreas Uhl
Abstract
As deepfakes become harder to detect by humans, more reliable detection methods are required to fight the spread of fake images and videos. In our work, we focus on PRNU-based detection methods, which, while popular in the image forensics scene, have not been given much attention in the context of deepfake detection. We adopt a PRNU-based approach originally developed for the detection of face morphs and facial retouching, and performed the first large scale test of PRNU-based deepfake detection methods on a variety of standard datasets. We show the impact of often neglected parameters of the face extraction stage on detection accuracy. We also document that existing PRNU-based methods cannot compete with state of the art methods based on deep learning but may be used to complement those in hybrid detection schemes.