Fake Image Detection

9 papers with code • 0 benchmarks • 2 datasets

( Image credit: FaceForensics++ )

Most implemented papers

FaceForensics++: Learning to Detect Manipulated Facial Images

ondyari/FaceForensics 25 Jan 2019

In particular, the benchmark is based on DeepFakes, Face2Face, FaceSwap and NeuralTextures as prominent representatives for facial manipulations at random compression level and size.

MesoNet: a Compact Facial Video Forgery Detection Network

DariusAf/MesoNet 4 Sep 2018

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.

ManTra-Net: Manipulation Tracing Network for Detection and Localization of Image Forgeries With Anomalous Features

ISICV/ManTraNet CVPR 2019

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.

Video Face Manipulation Detection Through Ensemble of CNNs

polimi-ispl/icpr2020dfdc 16 Apr 2020

In this paper, we tackle the problem of face manipulation detection in video sequences targeting modern facial manipulation techniques.

Learning to Detect Fake Face Images in the Wild

jesse1029/Fake-Face-Images-Detection-Tensorflow 24 Sep 2018

Although Generative Adversarial Network (GAN) can be used to generate the realistic image, improper use of these technologies brings hidden concerns.

Global Texture Enhancement for Fake Face Detection in the Wild

liuzhengzhe/Global_Texture_Enhancement_for_Fake_Face_Detection_in_the-Wild CVPR 2020

In this paper, we conduct an empirical study on fake/real faces, and have two important observations: firstly, the texture of fake faces is substantially different from real ones; secondly, global texture statistics are more robust to image editing and transferable to fake faces from different GANs and datasets.

FakePolisher: Making DeepFakes More Detection-Evasive by Shallow Reconstruction

hyhchaos/FakePolisher 13 Jun 2020

At this moment, GAN-based image generation methods are still imperfect, whose upsampling design has limitations in leaving some certain artifact patterns in the synthesized image.

Learning on Gradients: Generalized Artifacts Representation for GAN-Generated Images Detection

chuangchuangtan/lgrad CVPR 2023

The key of fake image detection is to develop a generalized representation to describe the artifacts produced by generation models.