Deepfake generation and detection: A benchmark and survey

G Pei, J Zhang, M Hu, Z Zhang, C Wang, Y Wu… - arXiv preprint arXiv …, 2024 - arxiv.org
Deepfake is a technology dedicated to creating highly realistic facial images and videos
under specific conditions, which has significant application potential in fields such as …

A Comprehensive Survey on Human Video Generation: Challenges, Methods, and Insights

W Lei, J Wang, F Ma, G Huang, L Liu - arXiv preprint arXiv:2407.08428, 2024 - arxiv.org
Human video generation is a dynamic and rapidly evolving task that aims to synthesize 2D
human body video sequences with generative models given control conditions such as text …

DiffTED: One-shot Audio-driven TED Talk Video Generation with Diffusion-based Co-speech Gestures

S Hogue, C Zhang, H Daruger… - Proceedings of the …, 2024 - openaccess.thecvf.com
Audio-driven talking video generation has advanced significantly but existing methods often
depend on video-to-video translation techniques and traditional generative networks like …

ProFake: Detecting Deepfakes in the Wild against Quality Degradation with Progressive Quality-adaptive Learning

H Xu, Y Wang, Z Wang, Z Ba, W Liu, L Jin… - Proceedings of the …, 2024 - dl.acm.org
Despite the promising advances in deepfake detection on current datasets, detecting visual
deepfakes in real-world scenarios (eg, deepfake videos and live streaming on YouTube) …

FFGAN: An Auto-supervised Approach for Frontal Face Generation Via Disentangled Contrastive Learning

G Wiem, D Ali - Procedia Computer Science, 2024 - Elsevier
This work proposes a novel generative model, FF-GAN (Frontal Face Generative Adversarial
Network), for generating high-quality and diverse frontal faces. FF-GAN utilizes contrastive …