Results 1 to 10 of about 815 (250)

Learning Local Texture and Global Frequency Clues for Face Forgery Detection [PDF]

Biomimetics
In recent years, the rapid advancement of deep learning techniques has significantly propelled the development of face forgery methods, drawing considerable attention to face forgery detection.
Xin Jin, Yuru Kou, Yuhao Xie, Yuying Zhao, Miss Laiha Mat Kiah, Qian Jiang, Wei Zhou   +6 more
doaj   +2 more sources

Combined spatial and frequency dual stream network for face forgery detection [PDF]

PeerJ Computer Science
With the development of generative model, the cost of facial manipulation and forgery is becoming lower and lower. Fraudulent data has brought numerous hidden threats in politics, privacy, and cybersecurity.
Hui Zhao, Xin Li, Bingxin Xu, Hongzhe Liu   +3 more
doaj   +3 more sources

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   +3 more sources

FreqMamba: Spatial–Frequency Fusion and State Space Sequence Modeling for Deepfake Detection [PDF]

Sensors
The rapid evolution of deepfake generation techniques has made high-fidelity facial manipulation a critical threat to social credibility and personal privacy, demanding detection algorithms with strong cross-domain generalization. Existing methods suffer
Zhiqi Li, Yajun Chen, Mingrui Li, Ruipeng Wang, Hao Liu   +4 more
doaj   +2 more sources

WAFF: A Synergetic Face Forgery Video Detection Method via Weakly Supervised EfficientNet [PDF]

Journal of Imaging
Deepfake detection has become an essential task for ensuring the authenticity and security of digital media. Although recent approaches have achieved notable progress, most existing detectors still exhibit limited generalization to unseen forgery ...
Zhengzhuo Pan, Bohan Chen, Longxiang Ma, Dawei Jin, Yu Zhou, Yudi Huang   +5 more
doaj   +2 more sources

Exposing Face Manipulation Based on Generative Adversarial Network–Transformer and Fake Frequency Noise Traces [PDF]

Sensors
In recent years, with the application of GANs and diffusion generative network algorithms, many highly realistic synthetic images are emerging, greatly increasing the potential for misuse, and deepfakes have become a serious social concern.
Qiaoyue Man, Young-Im Cho
doaj   +2 more sources

Learning to Discover Forgery Cues for Face Forgery Detection

IEEE Transactions on Information Forensics and Security
TIFS ...
Xi Wang, Jizhong Han, Xiaomeng Fu
exaly   +3 more sources

Multiple contexts and frequencies aggregation network for deepfake detection. [PDF]

PLoS ONE
Deepfake detection faces increasing challenges since the fast growth of generative models in developing massive and diverse Deepfake technologies. Recent advances rely on introducing heuristic features from spatial or frequency domains rather than ...
Zifeng Li, Wenzhong Tang, Shijun Gao, Yanyang Wang, Shuai Wang   +4 more
doaj   +2 more sources

TSFF-Net: A deep fake video detection model based on two-stream feature domain fusion. [PDF]

PLoS ONE
With the advancement of deep forgery techniques, particularly propelled by generative adversarial networks (GANs), identifying deepfake faces has become increasingly challenging.
Hangchuan Zhang, Caiping Hu, Shiyu Min, Hui Sui, Guola Zhou   +4 more
doaj   +2 more sources

Method of Face Forgery Detection Based on Self-Attention Capsule Network [PDF]

Jisuanji gongcheng, 2022
In recent years, face forgery is abused in fake videos, imposing a potential threat on the national, social and individual level, so face forgery detection is of great significance to individual privacy protection and national security.To improve the ...
LI Ke, LI Shaomei, JI Lixin, LIU Shuo
doaj   +1 more source