The creation and detection of deepfakes: A survey
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 …
difference between what is real and what is fake. In 2018, it was discovered how easy it is to …
X2face: A network for controlling face generation using images, audio, and pose codes
The objective of this paper is a neural network model that controls the pose and expression
of a given face, using another face or modality (eg audio). This model can then be used for …
of a given face, using another face or modality (eg audio). This model can then be used for …
One shot face swap** on megapixels
Face swap** has both positive applications such as entertainment, human-computer
interaction, etc., and negative applications such as DeepFake threats to politics, economics …
interaction, etc., and negative applications such as DeepFake threats to politics, economics …
Generative adversarial networks for image and video synthesis: Algorithms and applications
The generative adversarial network (GAN) framework has emerged as a powerful tool for
various image and video synthesis tasks, allowing the synthesis of visual content in an …
various image and video synthesis tasks, allowing the synthesis of visual content in an …
Advancing high fidelity identity swap** for forgery detection
In this work, we study various existing benchmarks for deepfake detection researches. In
particular, we examine a novel two-stage face swap** algorithm, called FaceShifter, for …
particular, we examine a novel two-stage face swap** algorithm, called FaceShifter, for …
Text-based editing of talking-head video
Editing talking-head video to change the speech content or to remove filler words is
challenging. We propose a novel method to edit talking-head video based on its transcript to …
challenging. We propose a novel method to edit talking-head video based on its transcript to …