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pro vyhledávání: '"Chen, Shen"'
Autor:
Yan, Zhiyuan, Yao, Taiping, Chen, Shen, Zhao, Yandan, Fu, Xinghe, Zhu, Junwei, Luo, Donghao, Yuan, Li, Wang, Chengjie, Ding, Shouhong, Wu, Yunsheng
We propose a new comprehensive benchmark to revolutionize the current deepfake detection field to the next generation. Predominantly, existing works identify top-notch detection algorithms and models by adhering to the common practice: training detec
Externí odkaz:
http://arxiv.org/abs/2406.13495
Autor:
Zhou, Xinghui, Zhou, Wenbo, Wei, Tianyi, Chen, Shen, Yao, Taiping, Ding, Shouhong, Zhang, Weiming, Yu, Nenghai
Face swapping has become a prominent research area in computer vision and image processing due to rapid technological advancements. The metric of measuring the quality in most face swapping methods relies on several distances between the manipulated
Externí odkaz:
http://arxiv.org/abs/2406.01884
In this paper, we propose a novel graph neural network-based recommendation model called KGLN, which leverages Knowledge Graph (KG) information to enhance the accuracy and effectiveness of personalized recommendations. We first use a single-layer neu
Externí odkaz:
http://arxiv.org/abs/2401.10244
Autor:
Lee, Chen-Shen
Publikováno v:
SciPost Phys. Core 7, 003 (2024)
The one-to-one relation between the winding number and the number of robust zero-energy edge states, known as bulk-boundary correspondence, is a celebrated feature of 1d systems with chiral symmetry. Although this property can be explained by the K-t
Externí odkaz:
http://arxiv.org/abs/2311.16801
The challenge in sourcing attribution for forgery faces has gained widespread attention due to the rapid development of generative techniques. While many recent works have taken essential steps on GAN-generated faces, more threatening attacks related
Externí odkaz:
http://arxiv.org/abs/2309.11132
Face forgery techniques have advanced rapidly and pose serious security threats. Existing face forgery detection methods try to learn generalizable features, but they still fall short of practical application. Additionally, finetuning these methods o
Externí odkaz:
http://arxiv.org/abs/2308.06217
Autor:
Sun, Ke, Chen, Shen, Yao, Taiping, Yang, Haozhe, Sun, Xiaoshuai, Ding, Shouhong, Ji, Rongrong
Deepfakes are realistic face manipulations that can pose serious threats to security, privacy, and trust. Existing methods mostly treat this task as binary classification, which uses digital labels or mask signals to train the detection model. We arg
Externí odkaz:
http://arxiv.org/abs/2307.16545