Improving Generalization in Facial Manipulation Detection Using Image Noise Residuals and Temporal Features

The high visual quality of modern deepfakes raises significant concerns about the trustworthiness of digital media and makes facial tampering detection more challenging. Although current deep learning-based deepfake detectors achieve excellent results when tested on deepfake images or image sequences generated using known methods, generalization—where a trained model is tasked with detecting deepfakes created with previously unseen manipulation techniques—is still a major challenge. In this paper, we investigate the impact of training spatial and spatio-temporal deep learning network architectures in the image noise residual domain using spatial rich model (SRM) filters on generalization performance. To this end, we conduct a series of tests on the manipulation methods of the FaceForensics++, DeeperForensics-1.0 and Celeb-DF datasets, demonstrating the value of image noise residuals and temporal feature exploitation in tackling the generalization task.