Results 1 to 10 of about 155 (110)
Learning to Discover Forgery Cues for Face Forgery Detection
IEEE Transactions on Information Forensics and SecurityTIFS ...
Jiahe Tian, Cai Yu, Xiaomeng Fu
exaly +3 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
Face Forgery Detection by 3D Decomposition [PDF]
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 Detecting digital face manipulation has attracted extensive attention due to fake media's potential harms to the public. However, recent advances have been able to reduce the forgery signals to a low magnitude. Decomposition, which reversibly decomposes an image into several constituent elements, is a promising way to highlight the hidden forgery ...
Xiangyu Zhu 0001 +4 more
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Survey on adversarial attacks and defense of face forgery and detection
网络与信息安全学报, 2023 Face forgery and detection has become a research hotspot.Face forgery methods can produce fake face images and videos.Some malicious videos, often targeting celebrities, are widely circulated on social networks, damaging the reputation of victims and ...
Shiyu HUANG, Feng YE, Tianqiang HUANG, Wei LI, Liqing HUANG, Haifeng LUO
doaj +3 more sources
Noise-attention-based forgery face detection method
网络与信息安全学报, 2023 With the advancement of artificial intelligence and deep neural networks, the ease of image generation and editing has increased significantly.Consequently, the occurrence of malicious tampering and forgery using image generation tools is on the rise ...
Bolin ZHANG +7 more
doaj +3 more sources
Representative Forgery Mining for Fake Face Detection [PDF]
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 Although vanilla Convolutional Neural Network (CNN) based detectors can achieve satisfactory performance on fake face detection, we observe that the detectors tend to seek forgeries on a limited region of face, which reveals that the detectors is short of understanding of forgery.
Chengrui Wang, Weihong Deng
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Face X-Ray for More General Face Forgery Detection [PDF]
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 Accepted to CVPR 2020 (Oral)
Lingzhi Li 0002 +6 more
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Survey on Generalization Methods of Face Forgery Detection [PDF]
Jisuanji kexue, 2022 The rapid development of deep learning technology provides powerful tools for the research of deepfake.Forged videos and images are more and more difficult for human eyes to distinguish between real and fake.Videos and images on the internet may have a ...
DONG Lin, HUANG Li-qing, YE Feng, HUANG Tian-qiang, WENG Bin, XU Chao
doaj +1 more source
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 +4 more
doaj +1 more source
CoRR, 2020 Rapid progress in deep learning is continuously making it easier and cheaper to generate video forgeries. Hence, it becomes very important to have a reliable way of detecting these forgeries. This paper describes such an approach for various tampering scenarios. The problem is modelled as a per-frame binary classification task.
Nika Dogonadze +2 more
openaire +2 more sources