Differences in forgery attributes of images generated in CNN-synthesized and image-editing
domains are large, and such differences make a unified image forgery detection and …

Supervised image-to-image translation has been proven to generate realistic images with
sharp details and to have good quantitative performance. Such methods are trained on a …

Disentangled Representation Learning (DRL) aims to learn a model capable of identifying
and disentangling the underlying factors hidden in the observable data in representation …

Facial editing is an important task in vision and graphics with numerous applications.
However, existing works are incapable to deliver a continuous and fine-grained editing …

Remarkable achievements have been attained with Generative Adversarial Networks
(GANs) in image-to-image translation. However, due to a tremendous amount of parameters …

Recent inversion methods have shown that real images can be inverted into StyleGAN's
latent space and numerous edits can be achieved on those images thanks to the …

We present a novel image inversion framework and a training pipeline to achieve high-
fidelity image inversion with high-quality attribute editing. Inverting real images into …

The widespread of generative models have called into question the authenticity of many
things on the web. In this situation, the task of image forensics is urgent. The existing …

Malicious applications of deepfakes (ie, technologies generating target facial attributes or
entire faces from facial images) have posed a huge threat to individuals' reputation and …

We propose VecGAN, an image-to-image translation framework for facial attribute editing
with interpretable latent directions. Facial attribute editing task faces the challenges of …