Results 41 to 50 of about 4,896,353 (133)

IIN-FFD: Intra-Inter Network for Face Forgery Detection

Tsinghua Science and Technology
Since different kinds of face forgeries leave similar forgery traces in videos, learning the common features from different kinds of forged faces would achieve promising generalization ability of forgery detection.
Qihua Zhou, Zhili Zhou, Zhipeng Bao, Weina Niu, Yuling Liu   +4 more
doaj   +1 more source

Towards efficient and effective face forgery detection

, 2022
Rapid progress in Face Forgery algorithms is becoming an increasingly relevant threat in the modern-day. To address the issue, multiple state-of-the-art forgery detection algorithms have been proposed.
Peng, Weixing
core  

Hierarchical Forgery Classifier On Multi-modality Face Forgery Clues

, 2023
Face forgery detection plays an important role in personal privacy and social security. With the development of adversarial generative models, high-quality forgery images become more and more indistinguishable from real to humans. Existing methods always
Peng, Chunlei, Liu, Decheng, Zheng, Zeyang, Gao, Xinbo, Wang, Yukai, Wang, Nannan   +5 more
core  

Local Relation Learning for Face Forgery Detection

, 2021
With the rapid development of facial manipulation techniques, face forgery has received considerable attention in digital media forensics due to security concerns.
Ji, Rongrong, Chen, Yang, Chen, Shen, Yao, Taiping, Li, Jilin, Ding, Shouhong   +5 more
core   +1 more source

Difficulty‑Aware Meta‑Learning for Cross‑Domain Face Forgery Detection

Nanjing Hangkong Hangtian Daxue Xuebao
With the rapid iteration of facial forgery techniques， robust detection mechanisms that can handle unseen forgery methods are increasingly crucial. However， current approaches are primarily tailored to specific forgery techniques， posing limitations in ...
JIN Shichen, TAN Xiaoyang
doaj   +1 more source

Learning spatial‐frequency interaction for generalizable deepfake detection

IET Image Processing
In recent years, face forgery detection has gained significant attention, resulting in considerable advancements. However, most existing methods rely on CNNs to extract artefacts from the spatial domain, overlooking the pervasive frequency‐domain ...
Tianbo Zhai, Kaiyin Lu, Jiajun Li, Yukai Wang, Wenjie Zhang, Peipeng Yu, Zhihua Xia   +6 more
doaj   +1 more source

A Large-scale Universal Evaluation Benchmark For Face Forgery Detection [PDF]

With the rapid development of AI-generated content (AIGC) technology, the production of realistic fake facial images and videos that deceive human visual perception has become possible.
Feng, Zunlei, Lou, Hengrui, Liu, Erteng, Song, Jie, Bei, Yijun, Cheng, Lechao, Geng, Jinsong, Song, Mingli   +7 more
core   +1 more source

ID3: Identity-Driven Learning Based on 3D Reconstruction and Frame-Level Residual Enhancement for Deepfakes Detection

IET Biometrics
With the rapid advancement of face manipulation technology, various forged videos of celebrities and politicians have appeared and cause pernicious social impact. In this light, forge video detection becomes a research hot spot recently.
Hui Ma, Jin Zhang, Xihong Chen, Wenhao Chu, Jiabao Guo, Junze Zheng, Liying Yang, Yanyan Liang   +7 more
doaj   +1 more source

Modal-Guided Multi-Domain Inconsistency Learning for Face Forgery Detection

Applied Sciences
The remarkable development of deepfake models has facilitated the generation of fake content with various modalities, such as forged images, manipulated audio, and modified video with (or without) corresponding audio.
Zishuo Guo, Baopeng Zhang, Jack Fan, Zhu Teng, Jianping Fan   +4 more
doaj   +1 more source

Face Forgery Detection with Long-Range Noise Features and Multilevel Frequency-Aware Clues

IET Biometrics
The widespread dissemination of high-fidelity fake faces created by face forgery techniques has caused serious trust concerns and ethical issues in modern society.
Yi Zhao, Xin Jin, Song Gao, Liwen Wu, Shaowen Yao, Qian Jiang   +5 more
doaj   +1 more source