Zobrazeno 1 - 10
of 46
pro vyhledávání: '"Liu, Ajian"'
Skeletal sequences, as well-structured representations of human behaviors, play a vital role in Human Activity Recognition (HAR). The transferability of adversarial skeletal sequences enables attacks in real-world HAR scenarios, such as autonomous dr
Externí odkaz:
http://arxiv.org/abs/2409.02483
Facial recognition systems are susceptible to both physical and digital attacks, posing significant security risks. Traditional approaches often treat these two attack types separately due to their distinct characteristics. Thus, when being combined
Externí odkaz:
http://arxiv.org/abs/2408.12793
Autor:
Su, Xinqi, Cui, Yawen, Liu, Ajian, Lin, Xun, Wang, Yuhao, Liang, Haochen, Li, Wenhui, Yu, Zitong
In current web environment, fake news spreads rapidly across online social networks, posing serious threats to society. Existing multimodal fake news detection (MFND) methods can be classified into knowledge-based and semantic-based approaches. Howev
Externí odkaz:
http://arxiv.org/abs/2408.10883
Autor:
Zou, Hang, Du, Chenxi, Liu, Ajian, Zhang, Yuan, Liu, Jing, Yang, Mingchuan, Wan, Jun, Zhang, Hui
Iris recognition is widely used in high-security scenarios due to its stability and distinctiveness. However, the acquisition of iris images typically requires near-infrared illumination and near-infrared band filters, leading to significant and cons
Externí odkaz:
http://arxiv.org/abs/2408.09752
Autor:
He, Xianhua, Liang, Dashuang, Yang, Song, Hao, Zhanlong, Ma, Hui, Mao, Binjie, Li, Xi, Wang, Yao, Yan, Pengfei, Liu, Ajian
Face recognition systems are frequently subjected to a variety of physical and digital attacks of different types. Previous methods have achieved satisfactory performance in scenarios that address physical attacks and digital attacks, respectively. H
Externí odkaz:
http://arxiv.org/abs/2404.08450
Autor:
Yuan, Haocheng, Liu, Ajian, Zheng, Junze, Wan, Jun, Deng, Jiankang, Escalera, Sergio, Escalante, Hugo Jair, Guyon, Isabelle, Lei, Zhen
Face Anti-Spoofing (FAS) is crucial to safeguard Face Recognition (FR) Systems. In real-world scenarios, FRs are confronted with both physical and digital attacks. However, existing algorithms often address only one type of attack at a time, which po
Externí odkaz:
http://arxiv.org/abs/2404.06211
Autor:
Liu, Ajian, Xue, Shuai, Gan, Jianwen, Wan, Jun, Liang, Yanyan, Deng, Jiankang, Escalera, Sergio, Lei, Zhen
Domain generalization (DG) based Face Anti-Spoofing (FAS) aims to improve the model's performance on unseen domains. Existing methods either rely on domain labels to align domain-invariant feature spaces, or disentangle generalizable features from th
Externí odkaz:
http://arxiv.org/abs/2403.14333
Autor:
Fang, Hao, Liu, Ajian, Yuan, Haocheng, Zheng, Junze, Zeng, Dingheng, Liu, Yanhong, Deng, Jiankang, Escalera, Sergio, Liu, Xiaoming, Wan, Jun, Lei, Zhen
Face Recognition (FR) systems can suffer from physical (i.e., print photo) and digital (i.e., DeepFake) attacks. However, previous related work rarely considers both situations at the same time. This implies the deployment of multiple models and thus
Externí odkaz:
http://arxiv.org/abs/2401.17699
Recently, vision transformer based multimodal learning methods have been proposed to improve the robustness of face anti-spoofing (FAS) systems. However, multimodal face data collected from the real world is often imperfect due to missing modalities
Externí odkaz:
http://arxiv.org/abs/2307.13958
Autor:
Liu, Ajian, Tan, Zichang, Yu, Zitong, Zhao, Chenxu, Wan, Jun, Liang, Yanyan, Lei, Zhen, Zhang, Du, Li, Stan Z., Guo, Guodong
The availability of handy multi-modal (i.e., RGB-D) sensors has brought about a surge of face anti-spoofing research. However, the current multi-modal face presentation attack detection (PAD) has two defects: (1) The framework based on multi-modal fu
Externí odkaz:
http://arxiv.org/abs/2305.03277