Results 11 to 20 of about 815 (250)
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
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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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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
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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
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Generalizing Face Forgery Detection with High-frequency Features [PDF]
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 Current face forgery detection methods achieve high accuracy under the within-database scenario where training and testing forgeries are synthesized by the same algorithm. However, few of them gain satisfying performance under the cross-database scenario where training and testing forgeries are synthesized by different algorithms.
Yuchen Luo +3 more
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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
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Local Relation Learning for Face Forgery Detection
Proceedings of the AAAI Conference on Artificial Intelligence, 2021 With the rapid development of facial manipulation techniques, face forgery has received considerable attention in digital media forensics due to security concerns. Most existing methods formulate face forgery detection as a classification problem and utilize binary labels or manipulated region masks as supervision.
Shen Chen 0004 +5 more
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Forgery Face Detection Based on Multi-scale Transformer Fusing Multi-domain Information [PDF]
Jisuanji kexue, 2023 At present,the proliferation of “face-changing” fake videos generated based on deep forgery technologies such as Deepfakes poses a considerable threat to citizens' privacy and national political security.Therefore,it is of great significance to study ...
MA Xin, JI Lixin, LI Shaomei
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