Search Results for author: Taiping Yao

Found 26 papers, 7 papers with code

Contrastive Pseudo Learning for Open-World DeepFake Attribution

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.

DeepFake Detection Face Swapping +1

Continual Face Forgery Detection via Historical Distribution Preserving

no code implementations11 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.

Knowledge Distillation

Towards General Visual-Linguistic Face Forgery Detection

no code implementations31 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.

Binary Classification DeepFake Detection +2

Instance-Aware Domain Generalization for Face Anti-Spoofing

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.

Domain Generalization Face Anti-Spoofing

Sibling-Attack: Rethinking Transferable Adversarial Attacks against Face Recognition

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.

Adversarial Attack Attribute +1

Adv-Attribute: Inconspicuous and Transferable Adversarial Attack on Face Recognition

no code implementations13 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.

Adversarial Attack Attribute +2

Adaptive Mixture of Experts Learning for Generalizable Face Anti-Spoofing

no code implementations20 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.

Domain Generalization Face Anti-Spoofing +1

Generative Domain Adaptation for Face Anti-Spoofing

no code implementations20 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.

Domain Adaptation Face Anti-Spoofing

Entropy-driven Sampling and Training Scheme for Conditional Diffusion Generation

1 code implementation23 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.

Conditional Image Generation Denoising

End-to-End Reconstruction-Classification Learning for Face Forgery Detection

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.

Classification

Exploiting Fine-grained Face Forgery Clues via Progressive Enhancement Learning

no code implementations28 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.

Dual Contrastive Learning for General Face Forgery Detection

no code implementations27 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.

Contrastive Learning

Spatiotemporal Inconsistency Learning for DeepFake Video Detection

no code implementations4 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).

Binary Classification Face Swapping

Adaptive Normalized Representation Learning for Generalizable Face Anti-Spoofing

no code implementations5 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.

Domain Generalization Face Anti-Spoofing +1

Structure Destruction and Content Combination for Face Anti-Spoofing

no code implementations22 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.

Face Anti-Spoofing Face Verification +1

Dual Reweighting Domain Generalization for Face Presentation Attack Detection

no code implementations30 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.

Domain Generalization Face Anti-Spoofing +1

Adv-Makeup: A New Imperceptible and Transferable Attack on Face Recognition

1 code implementation7 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.

Adversarial Attack Face Generation +2

Generalizable Representation Learning for Mixture Domain Face Anti-Spoofing

no code implementations6 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.

Domain Generalization Face Anti-Spoofing +2

Local Relation Learning for Face Forgery Detection

no code implementations6 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.

Relation

Delving into Data: Effectively Substitute Training for Black-box Attack

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.

Adversarial Attack

Multiple Granularity Group Interaction Prediction

no code implementations CVPR 2018 Taiping Yao, Minsi Wang, Bingbing Ni, Huawei Wei, Xiaokang Yang

Most human activity analysis works (i. e., recognition or prediction) only focus on a single granularity, i. e., either modelling global motion based on the coarse level movement such as human trajectories or forecasting future detailed action based on body parts’ movement such as skeleton motion.

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