In this paper, we propose a learning algorithm for detecting visual image manipulations that is trained only using a large dataset of real photographs.
This paper presents a method to automatically and efficiently detect face tampering in videos, and particularly focuses on two recent techniques used to generate hyper-realistic forged videos: Deepfake and Face2Face.
Based on this analysis, we demonstrate how the frequency representation can be used to identify deep fake images in an automated way, surpassing state-of-the-art methods.
Finally, the coarse localization information guides the model to further learn the finer local features and segment out the tampered region.
The elementary operation of cropping underpins nearly every computer vision system, ranging from data augmentation and translation invariance to computational photography and representation learning.
Automatically finding suspicious regions in a potentially forged image by splicing, inpainting or copy-move remains a widely open problem.
Due to limited computational and memory resources, current deep learning models accept only rather small images in input, calling for preliminary image resizing.