Easy access to audio-visual content on social media, combined with the availability of
modern tools such as Tensorflow or Keras, and open-source trained models, along with …

Generative deep learning algorithms have progressed to a point where it is difficult to tell the
difference between what is real and what is fake. In 2018, it was discovered how easy it is to …

Existing face forgery detectors mainly focus on specific forgery patterns like noise
characteristics, local textures, or frequency statistics for forgery detection. This causes …

AI-synthesized face-swapping videos, commonly known as DeepFakes, is an emerging
problem threatening the trustworthiness of online information. The need to develop and …

In this work we propose Identity Consistency Transformer, a novel face forgery detection
method that focuses on high-level semantics, specifically identity information, and detecting …

Abstract We present Face Swapping GAN (FSGAN) for face swapping and reenactment.
Unlike previous work, FSGAN is subject agnostic and can be applied to pairs of faces …

We propose an efficient framework, called Simple Swap (SimSwap), aiming for generalized
and high fidelity face swapping. In contrast to previous approaches that either lack the ability …

It is becoming increasingly easy to automatically replace a face of one person in a video with
the face of another person by using a pre-trained generative adversarial network (GAN) …

We study the problem of video-to-video synthesis, whose goal is to learn a mapping function
from an input source video (eg, a sequence of semantic segmentation masks) to an output …

In this work, we propose a novel two-stage framework, called FaceShifter, for high fidelity
and occlusion aware face swapping. Unlike many existing face swapping works that …