Results 61 to 70 of about 4,896,353 (133)

Federated Face Forgery Detection Learning with Personalized Representation [PDF]

Deep generator technology can produce high-quality fake videos that are indistinguishable, posing a serious social threat. Traditional forgery detection methods directly centralized training on data and lacked consideration of information sharing in non ...
Dang, Zhan, Peng, Chunlei, Liu, Decheng, Hu, Ruimin, Gao, Xinbo, Wang, Nannan   +5 more
core   +1 more source

UniForensics: Face Forgery Detection via General Facial Representation [PDF]

Previous deepfake detection methods mostly depend on low-level textural features vulnerable to perturbations and fall short of detecting unseen forgery methods.
Wan, Ming, Fang, Ziyuan, Wang, Zhanyi, Yu, Nenghai, Wei, Tianyi, Zhao, Hanqing, Zhou, Wenbo, Zhang, Weiming   +7 more
core   +1 more source

Face Forgery Detection with Elaborate Backbone

Face Forgery Detection (FFD), or Deepfake detection, aims to determine whether a digital face is real or fake. Due to different face synthesis algorithms with diverse forgery patterns, FFD models often overfit specific patterns in training datasets ...
Zheng, Haiyong, Shan, Shiguang, Liu, Yingjie, Zhang, Jie, Guo, Zonghui   +4 more
core  

Towards General Visual-Linguistic Face Forgery Detection [PDF]

Deepfakes are realistic face manipulations that can pose serious threats to security, privacy, and trust. Existing methods mostly treat this task as binary classification, which uses digital labels or mask signals to train the detection model.
Sun, Xiaoshuai, Ji, Rongrong, Yang, Haozhe, Sun, Ke, Chen, Shen, Yao, Taiping, Ding, Shouhong   +6 more
core   +1 more source

3D data augmentation and dual-branch model for robust face forgery detection

Graphical Models
We propose Dual-Branch Network (DBNet), a novel deepfake detection framework that addresses key limitations of existing works by jointly modeling 3D-temporal and fine-grained texture representations. Specifically, we aim to investigate how to (1) capture
Changshuang Zhou, Frederick W.B. Li, Chao Song, Dong Zheng, Bailin Yang   +4 more
doaj   +1 more source

Towards Intrinsic Common Discriminative Features Learning for Face Forgery Detection using Adversarial Learning

, 2022
Existing face forgery detection methods usually treat face forgery detection as a binary classification problem and adopt deep convolution neural networks to learn discriminative features.
Liu, Bin, Chu, Qi, Zhuang, Wanyi, Yu, Nenghai, Yuan, Haojie, Miao, Changtao   +5 more
core  

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   +1 more source

A New Reinforcement Learning Agent Framework for Digital Image Forgery Detection Using Prioritized Experience Replay

مجلة بغداد للعلوم
Digital image forgery detection has become an urgent and complex problem in an age when powerful editing tools can easily alter photographs. The familiar maxim ``a picture is worth a thousand words'' can no longer be taken at face value, since even ...
Muthana S. Mahdi, Saad N. Alsaad, Hasanen S. Abdullah   +2 more
doaj   +1 more source

Learning Expressive And Generalizable Motion Features For Face Forgery Detection

Previous face forgery detection methods mainly focus on appearance features, which may be easily attacked by sophisticated manipulation. Considering the majority of current face manipulation methods generate fake faces based on a single frame, which do ...
Zhang, Peng, Pu, Shiliang, Wang, Jingjing, Xie, Di, Zhang, Jingyi   +4 more
core  

Latent Spatiotemporal Adaptation for Generalized Face Forgery Video Detection [PDF]

Face forgery videos have caused severe public concerns, and many detectors have been proposed. However, most of these detectors suffer from limited generalization when detecting videos from unknown distributions, such as from unseen forgery methods.
Ge, Shiming, Xiao, Zihao, Li, Jianmin, Zhang, Daichi   +3 more
core   +1 more source