28 papers with code • 0 benchmarks • 3 datasets
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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.
ManTra-Net: Manipulation Tracing Network for Detection and Localization of Image Forgeries With Anomalous Features
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.
Detecting the camera model used to shoot a picture enables to solve a wide series of forensic problems, from copyright infringement to ownership attribution.
Image Forensics: Detecting duplication of scientific images with manipulation-invariant image similarity
Manipulation and re-use of images in scientific publications is a concerning problem that currently lacks a scalable solution.
Due to limited computational and memory resources, current deep learning models accept only rather small images in input, calling for preliminary image resizing.
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.