Results 151 to 160 of about 130,964 (204)

An Overview of Face Image Forgery Detection

Current Chinese Computer Science, 2022
: With the development of face forgery techniques, the spread and malicious abuse of forged images have become a thought-provoking problem, and the face forgery detection technique has also attracted people's attention. Academia has carried out in-depth research and discussion on detection techniques.
Defen He, Shuai Liu, Xin Jin, Shanshan Huang, Qian Jiang   +4 more
openaire   +1 more source

Visual-Semantic Transformer for Face Forgery Detection

2021 IEEE International Joint Conference on Biometrics (IJCB), 2021
This paper proposes a novel Visual-Semantic Transformer (VST) to detect face forgery based on semantic aware feature relations. In face images, intrinsic feature relations exist between different semantic parsing regions. We find that face forgery algorithms always change such relations. Therefore, we start the approach by extracting Contextual Feature
Yuting Xu, Gengyun Jia, Huaibo Huang, Junxian Duan, Ran He   +4 more
openaire   +1 more source

Adversarial Samples Generated by Self-Forgery for Face Forgery Detection

IEEE Transactions on Biometrics, Behavior, and Identity Science
As deep learning techniques continue to advance making face synthesis realistic and indistinguishable. Algorithms need to be continuously improved to cope with increasingly sophisticated forgery techniques.
Hanxian Duan, Qian Jiang, Xiaoyuan Xu, Yu Wang, Huasong Yi, Shaowen Yao, Xin Jin   +6 more
openaire   +2 more sources

Learning Patch-Channel Correspondence for Interpretable Face Forgery Detection

IEEE Transactions on Image Processing, 2023
Beyond high accuracy, good interpretability is very critical to deploy a face forgery detection model for visual content analysis. In this paper, we propose learning patch-channel correspondence to facilitate interpretable face forgery detection. Patch-channel correspondence aims to transform the latent features of a facial image into multi-channel ...
Yingying Hua, Ruixin Shi, Pengju Wang, Shiming Ge   +3 more
openaire   +2 more sources

Forensics Adapter: Adapting CLIP for Generalizable Face Forgery Detection

Computer Vision and Pattern Recognition
We describe the Forensics Adapter, an adapter network designed to transform CLIP into an effective and generalizable face forgery detector. Although CLIP is highly versatile, adapting it for face forgery detection is nontrivial as forgery-related ...
Xinjie Cui, Yuezun Li, Ao Luo, Jiaran Zhou, Junyu Dong   +4 more
semanticscholar   +1 more source

WMamba: Wavelet-based Mamba for Face Forgery Detection

ACM Multimedia
The rapid evolution of deepfake generation technologies necessitates the development of robust face forgery detection algorithms. Recent studies have demonstrated that wavelet analysis can enhance the generalization abilities of forgery detectors ...
Siran Peng, Tianshuo Zhang, Li Gao, Xiangyu Zhu, Haoyuan Zhang, Kai Pang, Zhen Lei   +6 more
semanticscholar   +1 more source

F2Trans: High-Frequency Fine-Grained Transformer for Face Forgery Detection

IEEE Transactions on Information Forensics and Security, 2023
In recent years, face forgery detectors have aroused great interest and achieved impressive performance, but they are still struggling with generalization and robustness.
Changtao Miao, Zichang Tan, Qi Chu, Huan Liu, Honggang Hu, Nenghai Yu   +5 more
semanticscholar   +1 more source

Semantic Token Transformer for Face Forgery Detection

IEEE Transactions on Information Forensics and Security
In the era of digital media, the proliferation of forged images and videos poses a significant threat to societal stability. With the rapid advancement of deep learning, the generation of realistic fake images has become increasingly simple, presenting ...
Chunlei Peng, Xiaoyi Luo, Decheng Liu, Nannan Wang, Ruimin Hu, Xinbo Gao   +5 more
openaire   +2 more sources

MLLM-Enhanced Face Forgery Detection: A Vision-Language Fusion Solution

arXiv.org
Reliable face forgery detection algorithms are crucial for countering the growing threat of deepfake-driven disinformation. Previous research has demonstrated the potential of Multimodal Large Language Models (MLLMs) in identifying manipulated faces ...
Siran Peng, Zipei Wang, Li Gao, Xiangyu Zhu, Tianshuo Zhang, Ajian Liu, Haoyuan Zhang, Zhen Lei   +7 more
semanticscholar   +1 more source

Face Forgery Detection Based on Fine-Grained Clues and Noise Inconsistency

IEEE Transactions on Artificial Intelligence
Deepfake detection has gained increasing research attention in media forensics, and a variety of works have been produced. However, subtle artifacts might be eliminated by compression, and the convolutional neural networks (CNNs)-based detectors are ...
Dengyong Zhang, Ruiyi He, Xin Liao, Feng Li, Jiaxin Chen, Gaobo Yang   +5 more
semanticscholar   +1 more source