Results 21 to 30 of about 130,964 (204)

DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection [PDF]

Computer Vision and Pattern Recognition, 2020
We present our on-going effort of constructing a large- scale benchmark for face forgery detection. The first version of this benchmark, DeeperForensics-1.0, represents the largest face forgery detection dataset by far, with 60, 000 videos constituted by
Liming Jiang, Wayne Wu, Ren Li, Chen Qian, Chen Change Loy   +4 more
semanticscholar   +1 more source

Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery Detection [PDF]

Computer Vision and Pattern Recognition, 2020
Although current deep learning-based face forgery detectors achieve impressive performance in constrained scenarios, they are vulnerable to samples created by unseen manipulation methods.
A. Haliassos, Konstantinos Vougioukas, Stavros Petridis, M. Pantic   +3 more
semanticscholar   +1 more source

Frequency-aware Discriminative Feature Learning Supervised by Single-Center Loss for Face Forgery Detection [PDF]

Computer Vision and Pattern Recognition, 2021
Face forgery detection is raising ever-increasing interest in computer vision since facial manipulation technologies cause serious worries. Though recent works have reached sound achievements, there are still unignorable problems: a) learned features ...
Jiaming Li, Hongtao Xie, Jiahong Li, Zhongyuan Wang, Yongdong Zhang   +4 more
semanticscholar   +1 more source

UIA-ViT: Unsupervised Inconsistency-Aware Method based on Vision Transformer for Face Forgery Detection [PDF]

European Conference on Computer Vision, 2022
Intra-frame inconsistency has been proved to be effective for the generalization of face forgery detection. However, learning to focus on these inconsistency requires extra pixel-level forged location annotations.
Wanyi Zhuang, Qi Chu, Zhentao Tan, Qiankun Liu, Haojie Yuan, Changtao Miao, Zixiang Luo, Nenghai Yu   +7 more
semanticscholar   +1 more source

Robust face forgery detection integrating local texture and global texture information

EURASIP Journal on Information Security
Facial forgery technology is advancing rapidly, leading to significant social security concerns. In recent years, as forgery technologies and types continue to emerge, many methods struggle to strike a balance between accuracy and robustness.
Rongrong Gong, Ruiyi He, Dengyong Zhang, Arun Kumar Sangaiah, Mohammed J. F. Alenazi   +4 more
doaj   +2 more sources

Survey on Generalization Methods of Face Forgery Detection [PDF]

Jisuanji kexue, 2022
The rapid development of deep learning technology provides powerful tools for the research of deepfake.Forged videos and images are more and more difficult for human eyes to distinguish between real and fake.Videos and images on the internet may have a ...
DONG Lin, HUANG Li-qing, YE Feng, HUANG Tian-qiang, WENG Bin, XU Chao
doaj   +1 more source

Multi-Feature Fusion Based Deepfake Face Forgery Video Detection

Systems, 2022
With the rapid development of deep learning, generating realistic fake face videos is becoming easier. It is common to make fake news, network pornography, extortion and other related illegal events using deep forgery.
Zhimao Lai, Yufei Wang, Renhai Feng, Xianglei Hu, Haifeng Xu   +4 more
doaj   +1 more source

Representative Forgery Mining for Fake Face Detection [PDF]

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021
Although vanilla Convolutional Neural Network (CNN) based detectors can achieve satisfactory performance on fake face detection, we observe that the detectors tend to seek forgeries on a limited region of face, which reveals that the detectors is short of understanding of forgery.
Wang, Chengrui, Deng, Weihong
openaire   +2 more sources

Beyond the Prior Forgery Knowledge: Mining Critical Clues for General Face Forgery Detection [PDF]

IEEE Transactions on Information Forensics and Security, 2023
Face forgery detection is essential in combating malicious digital face attacks. Previous methods mainly rely on prior expert knowledge to capture specific forgery clues, such as noise patterns, blending boundaries, and frequency artifacts.
Anwei Luo, Chen Kong, Jiwu Huang, Yongjian Hu, Xiangui Kang, A. Kot   +5 more
semanticscholar   +1 more source

CORE: Consistent Representation Learning for Face Forgery Detection [PDF]

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2022
Face manipulation techniques develop rapidly and arouse widespread public concerns. Despite that vanilla convolutional neural networks achieve acceptable performance, they suffer from the overfitting issue.
Yu-Shu Ni, Depu Meng, Changqian Yu, Chengbin Quan, Dongchun Ren, Youjian Zhao   +5 more
semanticscholar   +1 more source