Cross-Dataset Face Manipulation Detection
Burak Bekci, Zahid Akhtar, Hazım Kemal Ekenel · 2020
Easily available recent face image/video manipulation techniques and tools are now being utilized to generate highly realistic manipulated videos known as DeepFakes, which can fool face recognition systems and humans. Thus, it is vital to devise precise manipulation detection methods. Despite the progress, existing mechanisms are limited to the datasets or manipulation types. In this paper, to increase the performance under unseen data and manipulations, a DeepFakes detection framework using metric learning and steganalysis rich models is presented. Extensive empirical analysis on three publicly available datasets, namely, FaceForensics++, CelebDF, and DeepFakeTIMIT, were carried out to evaluate the generalization capability of the proposed approach. The framework attained 5% to 15% accuracy gains under unseen manipulations.