Results 31 to 40 of about 815 (250)

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

Multi-scale Wavelet Transformer for Face Forgery Detection

, 2023
Currently, many face forgery detection methods aggregate spatial and frequency features to enhance the generalization ability and gain promising performance under the cross-dataset scenario. However, these methods only leverage one level frequency information which limits their expressive ability. To overcome these limitations, we propose a multi-scale
Jie Liu, Jingjing Wang 0005, Peng Zhang 0075, Chunmao Wang, Di Xie, Shiliang Pu   +5 more
openaire   +2 more sources

Face Forgery Detection Based on the Improved Siamese Network

Security and Communication Networks, 2022
Face tampering is an intriguing task in video/image genuineness identification and has attracted significant amounts of attention in recent years. In this work, we propose a face forgery detection method that consists of preprocessing, an improved Siamese network-based feature extractor (including a feature alignment module), and postprocessing (a ...
Bo Wang 0024, Yucai Li, Xiaohan Wu, Yanyan Ma, Zengren Song, Mingkan Wu   +5 more
openaire   +1 more source

Poisoned Forgery Face: Towards Backdoor Attacks on Face Forgery Detection

CoRR
ICLR 2024 ...
Jiawei Liang, Siyuan Liang 0004, Aishan Liu, Xiaojun Jia, Junhao Kuang, Xiaochun Cao   +5 more
openaire   +3 more sources

Exploring Disentangled Content Information for Face Forgery Detection

, 2022
Convolutional neural network based face forgery detection methods have achieved remarkable results during training, but struggled to maintain comparable performance during testing. We observe that the detector is prone to focus more on content information than artifact traces, suggesting that the detector is sensitive to the intrinsic bias of the ...
Jiahao Liang, Huafeng Shi, Weihong Deng
openaire   +2 more sources

Exploring Frequency Adversarial Attacks for Face Forgery Detection

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022
Accepted by ...
Shuai Jia, Chao Ma 0004, Taiping Yao, Bangjie Yin, Shouhong Ding, Xiaokang Yang 0001   +5 more
openaire   +2 more sources

Revisiting Face Forgery Detection: From Facial Representation to Forgery Detection

IEEE Transactions on Pattern Analysis and Machine Intelligence
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, resulting in poor generalization to other unseen forgeries.
Guo, Zonghui, Liu, Yingjie, Zhang, Jie, Zheng, Haiyong, Shan, Shiguang   +4 more
openaire   +3 more sources

Learning to mask: Towards generalized face forgery detection

CoRR, 2022
Incorrect experimental ...
Fei, Jianwei, Dai, Yunshu, Wang, Huaming, Xia, Zhihua   +3 more
openaire   +2 more sources

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

CORE: Consistent Representation Learning for Face Forgery Detection

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2022
Accepted by CVPRW ...
Yunsheng Ni, Depu Meng, Changqian Yu, Chengbin Quan, Dongchun Ren, Youjian Zhao   +5 more
openaire   +2 more sources