Zobrazeno 1 - 10
of 464
pro vyhledávání: '"Yan, ZhiYuan"'
Autor:
Song, Wentang, Yan, Zhiyuan, Lin, Yuzhen, Yao, Taiping, Chen, Changsheng, Chen, Shen, Zhao, Yandan, Ding, Shouhong, Li, Bin
This paper addresses the generalization issue in deepfake detection by harnessing forgery quality in training data. Generally, the forgery quality of different deepfakes varies: some have easily recognizable forgery clues, while others are highly rea
Externí odkaz:
http://arxiv.org/abs/2411.05335
Autor:
Bai, Yonghong, Yan, Zhiyuan
Physical Unclonable Functions (PUFs) are widely used in key generation, with each PUF cell typically producing one bit of data. To enable the extraction of longer keys, a new non-binary response generation scheme based on the one-probability of PUF b
Externí odkaz:
http://arxiv.org/abs/2410.20324
Detecting deepfakes has become an important task. Most existing detection methods provide only real/fake predictions without offering human-comprehensible explanations. Recent studies leveraging MLLMs for deepfake detection have shown improvements in
Externí odkaz:
http://arxiv.org/abs/2410.06126
Three key challenges hinder the development of current deepfake video detection: (1) Temporal features can be complex and diverse: how can we identify general temporal artifacts to enhance model generalization? (2) Spatiotemporal models often lean he
Externí odkaz:
http://arxiv.org/abs/2408.17065
The generalization ability of deepfake detectors is vital for their applications in real-world scenarios. One effective solution to enhance this ability is to train the models with manually-blended data, which we termed "blendfake", encouraging model
Externí odkaz:
http://arxiv.org/abs/2408.17052
Learning intrinsic bias from limited data has been considered the main reason for the failure of deepfake detection with generalizability. Apart from the discovered content and specific-forgery bias, we reveal a novel spatial bias, where detectors in
Externí odkaz:
http://arxiv.org/abs/2408.06779
Autor:
Yan, Zhiyuan, Yao, Taiping, Chen, Shen, Zhao, Yandan, Fu, Xinghe, Zhu, Junwei, Luo, Donghao, Wang, Chengjie, Ding, Shouhong, Wu, Yunsheng, Yuan, Li
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:
Jia, Shan, Lyu, Reilin, Zhao, Kangran, Chen, Yize, Yan, Zhiyuan, Ju, Yan, Hu, Chuanbo, Li, Xin, Wu, Baoyuan, Lyu, Siwei
DeepFakes, which refer to AI-generated media content, have become an increasing concern due to their use as a means for disinformation. Detecting DeepFakes is currently solved with programmed machine learning algorithms. In this work, we investigate
Externí odkaz:
http://arxiv.org/abs/2403.14077
Assertion-based verification (ABV) is a critical method for ensuring design circuits comply with their architectural specifications, which are typically described in natural language. This process often requires human interpretation by verification e
Externí odkaz:
http://arxiv.org/abs/2402.00386
Transcending Forgery Specificity with Latent Space Augmentation for Generalizable Deepfake Detection
Deepfake detection faces a critical generalization hurdle, with performance deteriorating when there is a mismatch between the distributions of training and testing data. A broadly received explanation is the tendency of these detectors to be overfit
Externí odkaz:
http://arxiv.org/abs/2311.11278