Fake Image Detection
11 papers with code • 0 benchmarks • 2 datasets
( Image credit: FaceForensics++ )
Benchmarks
These leaderboards are used to track progress in Fake Image Detection
ArtiFact
Corn Seeds Dataset
Latest papers with no code
ASAP: Interpretable Analysis and Summarization of AI-generated Image Patterns at Scale
Generative image models have emerged as a promising technology to produce realistic images.
X-Transfer: A Transfer Learning-Based Framework for GAN-Generated Fake Image Detection
Generative adversarial networks (GANs) have remarkably advanced in diverse domains, especially image generation and editing.
Information-containing Adversarial Perturbation for Combating Facial Manipulation Systems
We use an encoder to map a facial image and its identity message to a cross-model adversarial example which can disrupt multiple facial manipulation systems to achieve initiative protection.
DE-FAKE: Detection and Attribution of Fake Images Generated by Text-to-Image Generation Models
To tackle this problem, we pioneer a systematic study on the detection and attribution of fake images generated by text-to-image generation models.
Evaluation of Pre-Trained CNN Models for Geographic Fake Image Detection
Thanks to the remarkable advances in generative adversarial networks (GANs), it is becoming increasingly easy to generate/manipulate images.
Geo-DefakeHop: High-Performance Geographic Fake Image Detection
A robust fake satellite image detection method, called Geo-DefakeHop, is proposed in this work.
Learning to Disentangle GAN Fingerprint for Fake Image Attribution
Adopting a multi-task framework, we propose a GAN Fingerprint Disentangling Network (GFD-Net) to simultaneously disentangle the fingerprint from GAN-generated images and produce a content-irrelevant representation for fake image attribution.
Imperceptible Adversarial Examples for Fake Image Detection
Fooling people with highly realistic fake images generated with Deepfake or GANs brings a great social disturbance to our society.
Fake-image detection with Robust Hashing
In this paper, we investigate whether robust hashing has a possibility to robustly detect fake-images even when multiple manipulation techniques such as JPEG compression are applied to images for the first time.
CycleGAN without checkerboard artifacts for counter-forensics of fake-image detection
In this paper, we propose a novel CycleGAN without checkerboard artifacts for counter-forensics of fake-image detection.
