Results 31 to 40 of about 4,896,353 (133)

Feature Focus: Towards Explainable and Transparent Deep Face Morphing Attack Detectors

Computers, 2021
Detecting morphed face images has become an important task to maintain the trust in automated verification systems based on facial images, e.g., at automated border control gates.
Clemens Seibold, Anna Hilsmann, Peter Eisert   +2 more
doaj   +1 more source

FPC‐Net: Learning to detect face forgery by adaptive feature fusion of patch correlation with CG‐Loss

IET Computer Vision, 2023
With the rapid development of manipulation technologies, the generation of Deep Fake videos is more accessible than ever. As a result, face forgery detection becomes a challenging task, attracting a significant amount of attention from researchers ...
Bin Wu, Lichao Su, Dan Chen, Yongli Cheng   +3 more
doaj   +1 more source

Research on the Face Forgery Detection Model Based on Adversarial Training and Disentanglement

Applied Sciences
With the advancement of generative models, face forgeries are becoming increasingly realistic, making face forgery detection a hot topic in research. The primary challenge in face forgery detection is the inadequate generalization performance.
Yidi Wang, Hui Fu, Tongkai Wu
doaj   +1 more source

FIT: Fine-Grained Identity-Aware Transformer for Generalizable Diffusion Face Forgery Detection

IEEE Access
Face forgery detection (FFD) is essential for the security and authenticity verification of digital media. Current FFD methods suffer from poor generalization to unseen face images created by diffusion models. Besides, they tend to rely on coarse-grained
Jiahe Zhang, Jiaqi Yang
doaj   +1 more source

STIDNet: Identity-Aware Face Forgery Detection with Spatiotemporal Knowledge Distillation [PDF]

The impressive development of facial manipulation techniques has raised severe public concerns. Identity-aware methods, especially suitable for protecting celebrities, are seen as one of promising face forgery detection approaches with additional ...
Hussain, Amir, Xie, Hongtao, Tan, Qingfeng, Fang, Mingqi, Yu, Lingyun, Tan, Zhiyuan, Wang, Zezheng, Tian, Zhihong, Li, Jiahong   +8 more
core   +1 more source

Mf-net: multi-feature fusion network based on two-stream extraction and multi-scale enhancement for face forgery detection

Complex & Intelligent Systems
Due to the increasing sophistication of face forgery techniques, the images generated are becoming more and more realistic and difficult for human eyes to distinguish.
Hanxian Duan, Qian Jiang, Xin Jin, Michal Wozniak, Yi Zhao, Liwen Wu, Shaowen Yao, Wei Zhou   +7 more
doaj   +1 more source

IR-capsule: two-stream network for face forgery detection. [PDF]

, 2023
With the emergence of deep learning, generating forged images or videos has become much easier in recent years. Face forgery detection, as a way to detect forgery, is an important topic in digital media forensics.
Zhao, Huimin, Zhu, Li, Han, Weihong, Li, Shudong, Lv, Jujian, Ren, Jinchang, Lin, Kaihan, Gu, Zhaoquan   +7 more
core   +1 more source

Identifying Rhythmic Patterns for Face Forgery Detection and Categorization

, 2022
With the emergence of GAN, face forgery technologies have been heavily abused. Achieving accurate face forgery detection is imminent. Inspired by remote photoplethysmography (rPPG) that PPG signal corresponds to the periodic change of skin color caused ...
Deng, Weihong, Liang, Jiahao
core  

MDCF-Net: Multi-Scale Dual-Branch Network for Compressed Face Forgery Detection

IEEE Access
Face forgery detection aims to identify manipulated or altered facial images or videos created using artificial intelligence. Existing detection methods exhibit favorable performance on high-quality videos, but the videos in daily applications are ...
Jiting Zhou, Xinrui Zhao, Qian Xu, Pu Zhang, Zhihao Zhou   +4 more
doaj   +1 more source

SHIELD: an evaluation benchmark for face spoofing and forgery detection with multimodal large language models

Visual Intelligence
Multimodal large language models (MLLMs) have demonstrated strong capabilities in vision-related tasks, capitalizing on their visual semantic comprehension and reasoning capabilities.
Yichen Shi, Yuhao Gao, Yingxin Lai, Hongyang Wang, Jun Feng, Lei He, Jun Wan, Changsheng Chen, Zitong Yu, Xiaochun Cao   +9 more
doaj   +1 more source