no code implementations • 28 Mar 2024 • Qianyu Zhou, Ke-Yue Zhang, Taiping Yao, Xuequan Lu, Shouhong Ding, Lizhuang Ma
Our method, consisting of Test-Time Style Projection (TTSP) and Diverse Style Shifts Simulation (DSSS), effectively projects the unseen data to the seen domain space.
1 code implementation • ICCV 2023 • Zhimin Sun, Shen Chen, Taiping Yao, Bangjie Yin, Ran Yi, Shouhong Ding, Lizhuang Ma
The challenge in sourcing attribution for forgery faces has gained widespread attention due to the rapid development of generative techniques.
no code implementations • 11 Aug 2023 • Ke Sun, Shen Chen, Taiping Yao, Xiaoshuai Sun, Shouhong Ding, Rongrong Ji
In this paper, we focus on a novel and challenging problem: Continual Face Forgery Detection (CFFD), which aims to efficiently learn from new forgery attacks without forgetting previous ones.
no code implementations • 31 Jul 2023 • Ke Sun, Shen Chen, Taiping Yao, Haozhe Yang, Xiaoshuai Sun, Shouhong Ding, Rongrong Ji
To address this issues, in this paper, we propose a novel paradigm named Visual-Linguistic Face Forgery Detection(VLFFD), which uses fine-grained sentence-level prompts as the annotation.
1 code implementation • CVPR 2023 • Qianyu Zhou, Ke-Yue Zhang, Taiping Yao, Xuequan Lu, Ran Yi, Shouhong Ding, Lizhuang Ma
To address these issues, we propose a novel perspective for DG FAS that aligns features on the instance level without the need for domain labels.
1 code implementation • CVPR 2023 • Zexin Li, Bangjie Yin, Taiping Yao, Juefeng Guo, Shouhong Ding, Simin Chen, Cong Liu
A hard challenge in developing practical face recognition (FR) attacks is due to the black-box nature of the target FR model, i. e., inaccessible gradient and parameter information to attackers.
no code implementations • 7 Dec 2022 • Yinpeng Dong, Peng Chen, Senyou Deng, Lianji L, Yi Sun, Hanyu Zhao, Jiaxing Li, Yunteng Tan, Xinyu Liu, Yangyi Dong, Enhui Xu, Jincai Xu, Shu Xu, Xuelin Fu, Changfeng Sun, Haoliang Han, Xuchong Zhang, Shen Chen, Zhimin Sun, Junyi Cao, Taiping Yao, Shouhong Ding, Yu Wu, Jian Lin, Tianpeng Wu, Ye Wang, Yu Fu, Lin Feng, Kangkang Gao, Zeyu Liu, Yuanzhe Pang, Chengqi Duan, Huipeng Zhou, Yajie Wang, Yuhang Zhao, Shangbo Wu, Haoran Lyu, Zhiyu Lin, YiFei Gao, Shuang Li, Haonan Wang, Jitao Sang, Chen Ma, Junhao Zheng, Yijia Li, Chao Shen, Chenhao Lin, Zhichao Cui, Guoshuai Liu, Huafeng Shi, Kun Hu, Mengxin Zhang
The security of artificial intelligence (AI) is an important research area towards safe, reliable, and trustworthy AI systems.
no code implementations • 13 Oct 2022 • Shuai Jia, Bangjie Yin, Taiping Yao, Shouhong Ding, Chunhua Shen, Xiaokang Yang, Chao Ma
For face recognition attacks, existing methods typically generate the l_p-norm perturbations on pixels, however, resulting in low attack transferability and high vulnerability to denoising defense models.
no code implementations • 20 Jul 2022 • Qianyu Zhou, Ke-Yue Zhang, Taiping Yao, Ran Yi, Kekai Sheng, Shouhong Ding, Lizhuang Ma
Most existing UDA FAS methods typically fit the trained models to the target domain via aligning the distribution of semantic high-level features.
no code implementations • 20 Jul 2022 • Qianyu Zhou, Ke-Yue Zhang, Taiping Yao, Ran Yi, Shouhong Ding, Lizhuang Ma
Existing DG-based FAS approaches always capture the domain-invariant features for generalizing on the various unseen domains.
1 code implementation • 23 Jun 2022 • Shengming Li, Guangcong Zheng, Hui Wang, Taiping Yao, Yang Chen, Shoudong Ding, Xi Li
Denoising Diffusion Probabilistic Model (DDPM) is able to make flexible conditional image generation from prior noise to real data, by introducing an independent noise-aware classifier to provide conditional gradient guidance at each time step of denoising process.
Ranked #1 on Conditional Image Generation on ImageNet 256x256
no code implementations • CVPR 2022 • Shuai Jia, Chao Ma, Taiping Yao, Bangjie Yin, Shouhong Ding, Xiaokang Yang
In addition, the proposed frequency attack enhances the transferability across face forgery detectors as black-box attacks.
1 code implementation • CVPR 2022 • Junyi Cao, Chao Ma, Taiping Yao, Shen Chen, Shouhong Ding, Xiaokang Yang
Reconstruction learning over real images enhances the learned representations to be aware of forgery patterns that are even unknown, while classification learning takes the charge of mining the essential discrepancy between real and fake images, facilitating the understanding of forgeries.
no code implementations • 28 Dec 2021 • Qiqi Gu, Shen Chen, Taiping Yao, Yang Chen, Shouhong Ding, Ran Yi
The progressive enhancement process facilitates the learning of discriminative features with fine-grained face forgery clues.
no code implementations • 27 Dec 2021 • Ke Sun, Taiping Yao, Shen Chen, Shouhong Ding, Jilin L, Rongrong Ji
With various facial manipulation techniques arising, face forgery detection has drawn growing attention due to security concerns.
no code implementations • 4 Sep 2021 • Zhihao Gu, Yang Chen, Taiping Yao, Shouhong Ding, Jilin Li, Feiyue Huang, Lizhuang Ma
To address this issue, we term this task as a Spatial-Temporal Inconsistency Learning (STIL) process and instantiate it into a novel STIL block, which consists of a Spatial Inconsistency Module (SIM), a Temporal Inconsistency Module (TIM), and an Information Supplement Module (ISM).
no code implementations • 5 Aug 2021 • Shubao Liu, Ke-Yue Zhang, Taiping Yao, Mingwei Bi, Shouhong Ding, Jilin Li, Feiyue Huang, Lizhuang Ma
However, little attention has been paid to the feature extraction process for the FAS task, especially the influence of normalization, which also has a great impact on the generalization of the learned representation.
no code implementations • 22 Jul 2021 • Ke-Yue Zhang, Taiping Yao, Jian Zhang, Shice Liu, Bangjie Yin, Shouhong Ding, Jilin Li
In pursuit of consolidating the face verification systems, prior face anti-spoofing studies excavate the hidden cues in original images to discriminate real persons and diverse attack types with the assistance of auxiliary supervision.
no code implementations • 30 Jun 2021 • Shubao Liu, Ke-Yue Zhang, Taiping Yao, Kekai Sheng, Shouhong Ding, Ying Tai, Jilin Li, Yuan Xie, Lizhuang Ma
Face anti-spoofing approaches based on domain generalization (DG) have drawn growing attention due to their robustness for unseen scenarios.
1 code implementation • 7 May 2021 • Bangjie Yin, Wenxuan Wang, Taiping Yao, Junfeng Guo, Zelun Kong, Shouhong Ding, Jilin Li, Cong Liu
Deep neural networks, particularly face recognition models, have been shown to be vulnerable to both digital and physical adversarial examples.
no code implementations • 6 May 2021 • Zhihong Chen, Taiping Yao, Kekai Sheng, Shouhong Ding, Ying Tai, Jilin Li, Feiyue Huang, Xinyu Jin
Face anti-spoofing approach based on domain generalization(DG) has drawn growing attention due to its robustness forunseen scenarios.
no code implementations • 6 May 2021 • Shen Chen, Taiping Yao, Yang Chen, Shouhong Ding, Jilin Li, Rongrong Ji
Specifically, we propose a Multi-scale Patch Similarity Module (MPSM), which measures the similarity between features of local regions and forms a robust and generalized similarity pattern.
no code implementations • CVPR 2021 • Wenxuan Wang, Bangjie Yin, Taiping Yao, Li Zhang, Yanwei Fu, Shouhong Ding, Jilin Li, Feiyue Huang, xiangyang xue
Previous substitute training approaches focus on stealing the knowledge of the target model based on real training data or synthetic data, without exploring what kind of data can further improve the transferability between the substitute and target models.
2 code implementations • 18 Feb 2021 • Liming Jiang, Zhengkui Guo, Wayne Wu, Zhaoyang Liu, Ziwei Liu, Chen Change Loy, Shuo Yang, Yuanjun Xiong, Wei Xia, Baoying Chen, Peiyu Zhuang, Sili Li, Shen Chen, Taiping Yao, Shouhong Ding, Jilin Li, Feiyue Huang, Liujuan Cao, Rongrong Ji, Changlei Lu, Ganchao Tan
This paper reports methods and results in the DeeperForensics Challenge 2020 on real-world face forgery detection.
no code implementations • 1 Feb 2021 • Jian Zhang, Ying Tai, Taiping Yao, Jia Meng, Shouhong Ding, Chengjie Wang, Jilin Li, Feiyue Huang, Rongrong Ji
Face authentication on mobile end has been widely applied in various scenarios.
no code implementations • ECCV 2020 • Ke-Yue Zhang, Taiping Yao, Jian Zhang, Ying Tai, Shouhong Ding, Jilin Li, Feiyue Huang, Haichuan Song, Lizhuang Ma
Face anti-spoofing is crucial to security of face recognition systems.
no code implementations • CVPR 2018 • Taiping Yao, Minsi Wang, Bingbing Ni, Huawei Wei, Xiaokang Yang
Most human activity analysis works (i. e., recognition orãprediction) only focus on a single granularity, i. e., eitherãmodelling global motion based on the coarse level movement such as human trajectories orãforecasting future detailed action based on body partsâ movement such as skeleton motion.