17 papers with code • 0 benchmarks • 3 datasets
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.
In this paper, we propose a learning algorithm for detecting visual image manipulations that is trained only using a large dataset of real photographs.
To fight against real-life image forgery, which commonly involves different types and combined manipulations, we propose a unified deep neural architecture called ManTra-Net.
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.
Due to limited computational and memory resources, current deep learning models accept only rather small images in input, calling for preliminary image resizing.
Automatically finding suspicious regions in a potentially forged image by splicing, inpainting or copy-move remains a widely open problem.