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.
Manipulation and re-use of images in scientific publications is a concerning problem that currently lacks a scalable solution.
Detecting the camera model used to shoot a picture enables to solve a wide series of forensic problems, from copyright infringement to ownership attribution.
Due to limited computational and memory resources, current deep learning models accept only rather small images in input, calling for preliminary image resizing.