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Improving Generalization by Commonality Learning in Face Forgery Detection
IEEE Transactions on Information Forensics and Security, 2022This paper proposes a commonality learning strategy for face video forgery detection to improve the generalization. Considering various face forgery methods could leave certain similar forgery traces in videos, we attempt to learn the common forgery ...
Peipeng Yu, Jianwei Fei, Zhihua Xia
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Forgery-Aware Adaptive Learning With Vision Transformer for Generalized Face Forgery Detection
IEEE Transactions on Circuits and Systems for Video TechnologyWith the rapid progress of generative models, the current challenge in face forgery detection is how to effectively detect realistic manipulated faces from different unseen domains.
Anwei Luo +6 more
exaly +2 more sources
IEEE Transactions on Circuits and Systems for Video Technology
Face forgery detection has become a new research hotspot. Though existing detection works have achieved impressive performance, they are difficult to achieve a proper trade-off between detection accuracy and model complexity.
Zhiqing Guo, Liejun Wang, Wenzhong Yang
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Face forgery detection has become a new research hotspot. Though existing detection works have achieved impressive performance, they are difficult to achieve a proper trade-off between detection accuracy and model complexity.
Zhiqing Guo, Liejun Wang, Wenzhong Yang
exaly +2 more sources
Face Forgery Detection via Multi-Feature Fusion and Local Enhancement
IEEE Transactions on Circuits and Systems for Video TechnologyWith the rapid growth of Internet technology, security concerns have risen, particularly with the prevalence of Deepfakes, a popular visual forgery technique. Therefore, there is necessary to research more powerful methods to detect Deepfakes.
Dengyong Zhang, Xin Liao, Feng Li
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Face Forgery Detection by 3D Decomposition and Composition Search
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023Detecting digital face manipulation has attracted extensive attention due to fake media's potential risks 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 +6 more
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Revisiting face forgery detection towards generalization
Neural NetworksFace forgery detection aims to distinguish AI generated fake faces with real faces. With the rapid development of face forgery creation algorithms, a large number of generative models have been proposed, which gradually reduce the local distortion phenomenon or the specific frequency traces in these models. At the same time, in the process of face data
Chunlei Peng +5 more
openaire +3 more sources
IEEE Transactions on Information Forensics and Security
With the continuous development of generative models on face generation, how to distinguish the real and fake face has become an important problem for security.
Chunlei Peng, Decheng Liu, Nannan Wang
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With the continuous development of generative models on face generation, how to distinguish the real and fake face has become an important problem for security.
Chunlei Peng, Decheng Liu, Nannan Wang
exaly +2 more sources
End-to-End Reconstruction-Classification Learning for Face Forgery Detection
Computer Vision and Pattern Recognition, 2022Existing face forgery detectors mainly focus on specific forgery patterns like noise characteristics, local textures, or frequency statistics for forgery detection. This causes specialization of learned representations to known forgery patterns presented
Junyi Cao +5 more
semanticscholar +1 more source
Thinking in Frequency: Face Forgery Detection by Mining Frequency-aware Clues
European Conference on Computer Vision, 2020As realistic facial manipulation technologies have achieved remarkable progress, social concerns about potential malicious abuse of these technologies bring out an emerging research topic of face forgery detection.
Yuyang Qian +4 more
semanticscholar +1 more source