3260 papers • 126 benchmarks • 313 datasets
This task has no description! Would you like to contribute one?
(Image credit: Papersgraph)
These leaderboards are used to track progress in gan-image-forensics-13
No benchmarks available.
Use these libraries to find gan-image-forensics-13 models and implementations
No datasets available.
No subtasks available.
This paper studies the ensembling of different trained Convolutional Neural Network (CNN) models and shows that combining these networks leads to promising face manipulation detection results on two publicly available datasets with more than 119000 videos.
A deep forgery discriminator (DeepFD) to efficiently and effectively detect the computer-generated images and adopt contrastive loss in seeking the typical features of the synthesized images generated by different GANs and follow by concatenating a classifier to detect such computer- generated images.
It is shown that it is possible to extract a compact feature vector from an image and this feature vector can be fed to an extremely simple classifier for GAN-generated image detection purpose.
A GAN simulator, AutoGAN, is proposed, which can simulate the artifacts produced by the common pipeline shared by several popular GAN models, and identifies a unique artifact caused by the up-sampling component included in the common GAN pipelines.
It is empirically shown that rarely activated neurons are related to the failure results of making diverse objects and inducing artifacts, and a correction method is suggested, called `Sequential Ablation’, to repair the defective part of the generated images without high computational cost and manual efforts.
Adding a benchmark result helps the community track progress.