Search Results for author:
Found 5 papers, 2 papers with code
FakeTracer: Proactively Defending Against Face-swap DeepFakes via Implanting Traces in Training
Face-swap DeepFake is an emerging AI-based face forgery technique that can replace the original face in a video with a generated face of the target identity while retaining consistent facial attributes such as expression and orientation.
DeepFake-o-meter: An Open Platform for DeepFake Detection
In recent years, the advent of deep learning-based techniques and the significant reduction in the cost of computation resulted in the feasibility of creating realistic videos of human faces, commonly known as DeepFakes.
Landmark Breaker: Obstructing DeepFake By Disturbing Landmark Extraction
In this paper, we describe Landmark Breaker, the first dedicated method to disrupt facial landmark extraction, and apply it to the obstruction of the generation of DeepFake videos. Our motivation is that disrupting the facial landmark extraction can affect the alignment of input face so as to degrade the DeepFake quality.
LandmarkGAN: Synthesizing Faces from Landmarks
Face synthesis is an important problem in computer vision with many applications.
Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics
AI-synthesized face-swapping videos, commonly known as DeepFakes, is an emerging problem threatening the trustworthiness of online information.
