Search Results for author: Shouhong Ding

Found 76 papers, 27 papers with code

SlerpFace: Face Template Protection via Spherical Linear Interpolation

no code implementations3 Jul 2024 Zhizhou Zhong, Yuxi Mi, Yuge Huang, Jianqing Xu, Guodong Mu, Shouhong Ding, Jingyun Zhang, rizen guo, Yunsheng Wu, Shuigeng Zhou

Based on studies of the diffusion model's generative capability, this paper proposes a defense to deteriorate the attack, by rotating templates to a noise-like distribution.

Face Recognition

DF40: Toward Next-Generation Deepfake Detection

no code implementations19 Jun 2024 Zhiyuan Yan, Taiping Yao, Shen Chen, Yandan Zhao, Xinghe Fu, Junwei Zhu, Donghao Luo, Li Yuan, Chengjie Wang, Shouhong Ding, Yunsheng Wu

In this work, we found the dataset (both train and test) can be the "primary culprit" due to: (1) forgery diversity: Deepfake techniques are commonly referred to as both face forgery (face-swapping and face-reenactment) and entire image synthesis (AIGC).

DeepFake Detection Face Reenactment +2

Rank-based No-reference Quality Assessment for Face Swapping

no code implementations4 Jun 2024 Xinghui Zhou, Wenbo Zhou, Tianyi Wei, Shen Chen, Taiping Yao, Shouhong Ding, Weiming Zhang, Nenghai Yu

Extensive experiments confirm the superiority of our method over existing general no-reference image quality assessment metrics and the latest metric of facial image quality assessment, making it well suited for evaluating face swapping images in real-world scenarios.

Face Swapping No-Reference Image Quality Assessment +1

Dual Relation Mining Network for Zero-Shot Learning

no code implementations6 May 2024 Jinwei Han, Yingguo Gao, Zhiwen Lin, Ke Yan, Shouhong Ding, Yuan Gao, Gui-Song Xia

Specifically, we introduce a Dual Attention Block (DAB) for visual-semantic relationship mining, which enriches visual information by multi-level feature fusion and conducts spatial attention for visual to semantic embedding.

Attribute Relation +2

Rethinking Clothes Changing Person ReID: Conflicts, Synthesis, and Optimization

no code implementations19 Apr 2024 Junjie Li, Guanshuo Wang, Fufu Yu, Yichao Yan, Qiong Jia, Shouhong Ding, Xingdong Sheng, Yunhui Liu, Xiaokang Yang

However, such improvement sacrifices the performance under the standard protocol, caused by the inner conflict between standard and CC.

Clothes Changing Person Re-Identification

Anchor-based Robust Finetuning of Vision-Language Models

no code implementations CVPR 2024 Jinwei Han, Zhiwen Lin, Zhongyisun Sun, Yingguo Gao, Ke Yan, Shouhong Ding, Yuan Gao, Gui-Song Xia

Specifically, two types of anchors are elaborated in our method, including i) text-compensated anchor which uses the images from the finetune set but enriches the text supervision from a pretrained captioner, ii) image-text-pair anchor which is retrieved from the dataset similar to pretraining data of CLIP according to the downstream task, associating with the original CLIP text with rich semantics.

Language Modelling Zero-Shot Learning

SDPose: Tokenized Pose Estimation via Circulation-Guide Self-Distillation

1 code implementation CVPR 2024 Sichen Chen, Yingyi Zhang, Siming Huang, Ran Yi, Ke Fan, Ruixin Zhang, Peixian Chen, Jun Wang, Shouhong Ding, Lizhuang Ma

To mitigate the problem of under-fitting, we design a transformer module named Multi-Cycled Transformer(MCT) based on multiple-cycled forwards to more fully exploit the potential of small model parameters.

Edge-computing Pose Estimation

Test-Time Domain Generalization for Face Anti-Spoofing

no code implementations CVPR 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.

Domain Generalization Face Anti-Spoofing

LaRE^2: Latent Reconstruction Error Based Method for Diffusion-Generated Image Detection

no code implementations CVPR 2024 Yunpeng Luo, Junlong Du, Ke Yan, Shouhong Ding

In response to this, we propose a novel Latent REconstruction error guided feature REfinement method (LaRE^2) for detecting the diffusion-generated images.

Image Generation

Privacy-Preserving Face Recognition Using Trainable Feature Subtraction

1 code implementation CVPR 2024 Yuxi Mi, Zhizhou Zhong, Yuge Huang, Jiazhen Ji, Jianqing Xu, Jun Wang, Shaoming Wang, Shouhong Ding, Shuigeng Zhou

Recognizable identity features within the image are encouraged by co-training a recognition model on its high-dimensional feature representation.

Face Recognition Image Compression +1

Model Tailor: Mitigating Catastrophic Forgetting in Multi-modal Large Language Models

no code implementations19 Feb 2024 Didi Zhu, Zhongyi Sun, Zexi Li, Tao Shen, Ke Yan, Shouhong Ding, Kun Kuang, Chao Wu

Catastrophic forgetting emerges as a critical challenge when fine-tuning multi-modal large language models (MLLMs), where improving performance on unseen tasks often leads to a significant performance drop on the original tasks.

Image Captioning Question Answering +1

Re-thinking Data Availability Attacks Against Deep Neural Networks

no code implementations CVPR 2024 Bin Fang, Bo Li, Shuang Wu, Shouhong Ding, Ran Yi, Lizhuang Ma

In this paper we re-examine the existing availability attack methods and propose a novel two-stage min-max-min optimization paradigm to generate robust unlearnable noise.

Rethinking Generalizable Face Anti-spoofing via Hierarchical Prototype-guided Distribution Refinement in Hyperbolic Space

no code implementations CVPR 2024 Chengyang Hu, Ke-Yue Zhang, Taiping Yao, Shouhong Ding, Lizhuang Ma

In detail we propose the Hierarchical Prototype Learning to simultaneously guide domain alignment and improve the discriminative ability via constraining the multi-level relations between prototypes and instances in hyperbolic space.

Domain Generalization Face Anti-Spoofing

MmAP : Multi-modal Alignment Prompt for Cross-domain Multi-task Learning

no code implementations14 Dec 2023 Yi Xin, Junlong Du, Qiang Wang, Ke Yan, Shouhong Ding

On the one hand, to maximize the complementarity of tasks with high similarity, we utilize a gradient-driven task grouping method that partitions tasks into several disjoint groups and assign a group-shared MmAP to each group.

Decoder Language Modelling +2

Combining Past, Present and Future: A Self-Supervised Approach for Class Incremental Learning

no code implementations15 Nov 2023 Xiaoshuang Chen, Zhongyi Sun, Ke Yan, Shouhong Ding, Hongtao Lu

In detail, CPPF consists of a prototype clustering module (PC), an embedding space reserving module (ESR) and a multi-teacher distillation module (MTD).

Class Incremental Learning Incremental Learning

Exploring Decision-based Black-box Attacks on Face Forgery Detection

no code implementations18 Oct 2023 Zhaoyu Chen, Bo Li, Kaixun Jiang, Shuang Wu, Shouhong Ding, Wenqiang Zhang

Further, the fake faces by our method can pass face forgery detection and face recognition, which exposes the security problems of face forgery detectors.

Face Recognition

Generalizable Person Search on Open-world User-Generated Video Content

no code implementations16 Oct 2023 Junjie Li, Guanshuo Wang, Yichao Yan, Fufu Yu, Qiong Jia, Jie Qin, Shouhong Ding, Xiaokang Yang

Person search is a challenging task that involves detecting and retrieving individuals from a large set of un-cropped scene images.

Domain Generalization Person Search

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

SoccerNet 2023 Challenges Results

2 code implementations12 Sep 2023 Anthony Cioppa, Silvio Giancola, Vladimir Somers, Floriane Magera, Xin Zhou, Hassan Mkhallati, Adrien Deliège, Jan Held, Carlos Hinojosa, Amir M. Mansourian, Pierre Miralles, Olivier Barnich, Christophe De Vleeschouwer, Alexandre Alahi, Bernard Ghanem, Marc Van Droogenbroeck, Abdullah Kamal, Adrien Maglo, Albert Clapés, Amr Abdelaziz, Artur Xarles, Astrid Orcesi, Atom Scott, Bin Liu, Byoungkwon Lim, Chen Chen, Fabian Deuser, Feng Yan, Fufu Yu, Gal Shitrit, Guanshuo Wang, Gyusik Choi, Hankyul Kim, Hao Guo, Hasby Fahrudin, Hidenari Koguchi, Håkan Ardö, Ibrahim Salah, Ido Yerushalmy, Iftikar Muhammad, Ikuma Uchida, Ishay Be'ery, Jaonary Rabarisoa, Jeongae Lee, Jiajun Fu, Jianqin Yin, Jinghang Xu, Jongho Nang, Julien Denize, Junjie Li, Junpei Zhang, Juntae Kim, Kamil Synowiec, Kenji Kobayashi, Kexin Zhang, Konrad Habel, Kota Nakajima, Licheng Jiao, Lin Ma, Lizhi Wang, Luping Wang, Menglong Li, Mengying Zhou, Mohamed Nasr, Mohamed Abdelwahed, Mykola Liashuha, Nikolay Falaleev, Norbert Oswald, Qiong Jia, Quoc-Cuong Pham, Ran Song, Romain Hérault, Rui Peng, Ruilong Chen, Ruixuan Liu, Ruslan Baikulov, Ryuto Fukushima, Sergio Escalera, Seungcheon Lee, Shimin Chen, Shouhong Ding, Taiga Someya, Thomas B. Moeslund, Tianjiao Li, Wei Shen, Wei zhang, Wei Li, Wei Dai, Weixin Luo, Wending Zhao, Wenjie Zhang, Xinquan Yang, Yanbiao Ma, Yeeun Joo, Yingsen Zeng, Yiyang Gan, Yongqiang Zhu, Yujie Zhong, Zheng Ruan, Zhiheng Li, Zhijian Huang, Ziyu Meng

More information on the tasks, challenges, and leaderboards are available on https://www. soccer-net. org.

Action Spotting Camera Calibration +4

Privacy-Preserving Face Recognition Using Random Frequency Components

1 code implementation ICCV 2023 Yuxi Mi, Yuge Huang, Jiazhen Ji, Minyi Zhao, Jiaxiang Wu, Xingkun Xu, Shouhong Ding, Shuigeng Zhou

The ubiquitous use of face recognition has sparked increasing privacy concerns, as unauthorized access to sensitive face images could compromise the information of individuals.

Face Recognition Privacy Preserving

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

Re-thinking Data Availablity Attacks Against Deep Neural Networks

no code implementations18 May 2023 Bin Fang, Bo Li, Shuang Wu, Ran Yi, Shouhong Ding, Lizhuang Ma

The unauthorized use of personal data for commercial purposes and the clandestine acquisition of private data for training machine learning models continue to raise concerns.

Towards Generalizable Data Protection With Transferable Unlearnable Examples

no code implementations18 May 2023 Bin Fang, Bo Li, Shuang Wu, Tianyi Zheng, Shouhong Ding, Ran Yi, Lizhuang Ma

One of the crucial factors contributing to this success has been the access to an abundance of high-quality data for constructing machine learning models.

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 +1

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

Exploiting the Textual Potential from Vision-Language Pre-training for Text-based Person Search

no code implementations8 Mar 2023 Guanshuo Wang, Fufu Yu, Junjie Li, Qiong Jia, Shouhong Ding

Text-based Person Search (TPS), is targeted on retrieving pedestrians to match text descriptions instead of query images.

Attribute Person Search +2

DistilPose: Tokenized Pose Regression with Heatmap Distillation

1 code implementation CVPR 2023 Suhang Ye, Yingyi Zhang, Jie Hu, Liujuan Cao, Shengchuan Zhang, Lei Shen, Jun Wang, Shouhong Ding, Rongrong Ji

Specifically, DistilPose maximizes the transfer of knowledge from the teacher model (heatmap-based) to the student model (regression-based) through Token-distilling Encoder (TDE) and Simulated Heatmaps.

Knowledge Distillation Pose Estimation +1

Delving into the Adversarial Robustness of Federated Learning

no code implementations19 Feb 2023 Jie Zhang, Bo Li, Chen Chen, Lingjuan Lyu, Shuang Wu, Shouhong Ding, Chao Wu

In this work, we propose a novel algorithm called Decision Boundary based Federated Adversarial Training (DBFAT), which consists of two components (local re-weighting and global regularization) to improve both accuracy and robustness of FL systems.

Adversarial Robustness Federated Learning

Probabilistic Knowledge Distillation of Face Ensembles

no code implementations CVPR 2023 Jianqing Xu, Shen Li, Ailin Deng, Miao Xiong, Jiaying Wu, Jiaxiang Wu, Shouhong Ding, Bryan Hooi

Mean ensemble (i. e. averaging predictions from multiple models) is a commonly-used technique in machine learning that improves the performance of each individual model.

Face Image Quality Face Recognition +2

Shape Matters: Deformable Patch Attack

1 code implementation European Conference on Computer Vision 2022 Zhaoyu Chen, Bo Li, Shuang Wu, Jianghe Xu, Shouhong Ding, Wenqiang Zhang

Though deep neural networks (DNNs) have demonstrated excellent performance in computer vision, they are susceptible and vulnerable to carefully crafted adversarial examples which can mislead DNNs to incorrect outputs.

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

SoccerNet 2022 Challenges Results

7 code implementations5 Oct 2022 Silvio Giancola, Anthony Cioppa, Adrien Deliège, Floriane Magera, Vladimir Somers, Le Kang, Xin Zhou, Olivier Barnich, Christophe De Vleeschouwer, Alexandre Alahi, Bernard Ghanem, Marc Van Droogenbroeck, Abdulrahman Darwish, Adrien Maglo, Albert Clapés, Andreas Luyts, Andrei Boiarov, Artur Xarles, Astrid Orcesi, Avijit Shah, Baoyu Fan, Bharath Comandur, Chen Chen, Chen Zhang, Chen Zhao, Chengzhi Lin, Cheuk-Yiu Chan, Chun Chuen Hui, Dengjie Li, Fan Yang, Fan Liang, Fang Da, Feng Yan, Fufu Yu, Guanshuo Wang, H. Anthony Chan, He Zhu, Hongwei Kan, Jiaming Chu, Jianming Hu, Jianyang Gu, Jin Chen, João V. B. Soares, Jonas Theiner, Jorge De Corte, José Henrique Brito, Jun Zhang, Junjie Li, Junwei Liang, Leqi Shen, Lin Ma, Lingchi Chen, Miguel Santos Marques, Mike Azatov, Nikita Kasatkin, Ning Wang, Qiong Jia, Quoc Cuong Pham, Ralph Ewerth, Ran Song, RenGang Li, Rikke Gade, Ruben Debien, Runze Zhang, Sangrok Lee, Sergio Escalera, Shan Jiang, Shigeyuki Odashima, Shimin Chen, Shoichi Masui, Shouhong Ding, Sin-wai Chan, Siyu Chen, Tallal El-Shabrawy, Tao He, Thomas B. Moeslund, Wan-Chi Siu, Wei zhang, Wei Li, Xiangwei Wang, Xiao Tan, Xiaochuan Li, Xiaolin Wei, Xiaoqing Ye, Xing Liu, Xinying Wang, Yandong Guo, YaQian Zhao, Yi Yu, YingYing Li, Yue He, Yujie Zhong, Zhenhua Guo, Zhiheng Li

The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team.

Action Spotting Camera Calibration +3

Adma-GAN: Attribute-Driven Memory Augmented GANs for Text-to-Image Generation

no code implementations28 Sep 2022 Xintian Wu, Hanbin Zhao, Liangli Zheng, Shouhong Ding, Xi Li

Existing methods mainly extract the text information from only one sentence to represent an image and the text representation effects the quality of the generated image well.

Attribute Sentence +1

Towards Stable Co-saliency Detection and Object Co-segmentation

no code implementations25 Sep 2022 Bo Li, Lv Tang, Senyun Kuang, Mofei Song, Shouhong Ding

In this paper, we present a novel model for simultaneous stable co-saliency detection (CoSOD) and object co-segmentation (CoSEG).

Object Saliency Detection +1

ECO-TR: Efficient Correspondences Finding Via Coarse-to-Fine Refinement

1 code implementation25 Sep 2022 Dongli Tan, Jiang-Jiang Liu, Xingyu Chen, Chao Chen, Ruixin Zhang, Yunhang Shen, Shouhong Ding, Rongrong Ji

In this paper, we propose an efficient structure named Efficient Correspondence Transformer (ECO-TR) by finding correspondences in a coarse-to-fine manner, which significantly improves the efficiency of functional correspondence model.

Outlier Detection

Federated Learning with Label Distribution Skew via Logits Calibration

2 code implementations1 Sep 2022 Jie Zhang, Zhiqi Li, Bo Li, Jianghe Xu, Shuang Wu, Shouhong Ding, Chao Wu

Extensive experiments on federated datasets and real-world datasets demonstrate that FedLC leads to a more accurate global model and much improved performance.

Federated Learning

Domain Adaptive Person Search

2 code implementations25 Jul 2022 Junjie Li, Yichao Yan, Guanshuo Wang, Fufu Yu, Qiong Jia, Shouhong Ding

In this paper, we take a further step and present Domain Adaptive Person Search (DAPS), which aims to generalize the model from a labeled source domain to the unlabeled target domain.

Pedestrian Detection Person Re-Identification +1

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

DuetFace: Collaborative Privacy-Preserving Face Recognition via Channel Splitting in the Frequency Domain

1 code implementation15 Jul 2022 Yuxi Mi, Yuge Huang, Jiazhen Ji, Hongquan Liu, Xingkun Xu, Shouhong Ding, Shuigeng Zhou

To compensate, the method introduces a plug-in interactive block to allow attention transfer from the client-side by producing a feature mask.

Collaborative Inference Face Recognition +1

Evaluation-oriented Knowledge Distillation for Deep Face Recognition

1 code implementation CVPR 2022 Yuge Huang, Jiaxiang Wu, Xingkun Xu, Shouhong Ding

Inspired by the ultimate goal of KD methods, we propose a novel Evaluation oriented KD method (EKD) for deep face recognition to directly reduce the performance gap between the teacher and student models during training.

Face Recognition Knowledge Distillation +1

ContrastMask: Contrastive Learning to Segment Every Thing

1 code implementation CVPR 2022 Xuehui Wang, Kai Zhao, Ruixin Zhang, Shouhong Ding, Yan Wang, Wei Shen

In this framework, annotated masks of seen categories and pseudo masks of unseen categories serve as a prior for contrastive learning, where features from the mask regions (foreground) are pulled together, and are contrasted against those from the background, and vice versa.

Instance Segmentation Segmentation +1

Towards Practical Certifiable Patch Defense with Vision Transformer

no code implementations CVPR 2022 Zhaoyu Chen, Bo Li, Jianghe Xu, Shuang Wu, Shouhong Ding, Wenqiang Zhang

To move towards a practical certifiable patch defense, we introduce Vision Transformer (ViT) into the framework of Derandomized Smoothing (DS).

Geometric Synthesis: A Free lunch for Large-scale Palmprint Recognition Model Pretraining

no code implementations11 Mar 2022 Kai Zhao, Lei Shen, Yingyi Zhang, Chuhan Zhou, Tao Wang, Ruixin Zhang, Shouhong Ding, Wei Jia, Wei Shen

In this paper, by observing that palmar creases are the key information to deep-learning-based palmprint recognition, we propose to synthesize training data by manipulating palmar creases.


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 Decoder

Detecting Camouflaged Object in Frequency Domain

1 code implementation CVPR 2022 Yijie Zhong, Bo Li, Lv Tang, Senyun Kuang, Shuang Wu, Shouhong Ding

We first design a novel frequency enhancement module (FEM) to dig clues of camouflaged objects in the frequency domain.

Object object-detection +1

Towards Efficient Data Free Black-Box Adversarial Attack

1 code implementation CVPR 2022 Jie Zhang, Bo Li, Jianghe Xu, Shuang Wu, Shouhong Ding, Lei Zhang, Chao Wu

The proposed method can efficiently imitate the target model through a small number of queries and achieve high attack success rate.

Adversarial Attack

Feature Generation and Hypothesis Verification for Reliable Face Anti-Spoofing

1 code implementation30 Dec 2021 Shice Liu, Shitao Lu, Hongyi Xu, Jing Yang, Shouhong Ding, Lizhuang Ma

However, the improvement is still limited by two issues: 1) It is difficult to perfectly map all faces to a shared feature space.

Disentanglement Domain Generalization +1

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

DENSE: Data-Free One-Shot Federated Learning

1 code implementation23 Dec 2021 Jie Zhang, Chen Chen, Bo Li, Lingjuan Lyu, Shuang Wu, Shouhong Ding, Chunhua Shen, Chao Wu

One-shot Federated Learning (FL) has recently emerged as a promising approach, which allows the central server to learn a model in a single communication round.

Federated Learning

Highly Efficient Natural Image Matting

no code implementations25 Oct 2021 Yijie Zhong, Bo Li, Lv Tang, Hao Tang, Shouhong Ding

With a lightweight basic convolution block, we build a two-stages framework: Segmentation Network (SN) is designed to capture sufficient semantics and classify the pixels into unknown, foreground and background regions; Matting Refine Network (MRN) aims at capturing detailed texture information and regressing accurate alpha values.

Image Matting

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

Disentangled High Quality Salient Object Detection

2 code implementations ICCV 2021 Lv Tang, Bo Li, Shouhong Ding, Mofei Song

As a pixel-wise classification task, LRSCN is designed to capture sufficient semantics at low-resolution to identify the definite salient, background and uncertain image regions.

Object object-detection +4

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.


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

Anomaly Detection by One Class Latent Regularized Networks

no code implementations5 Feb 2020 Chengwei Chen, Pan Chen, Haichuan Song, Yiqing Tao, Yuan Xie, Shouhong Ding, Lizhuang Ma

Anomaly detection is a fundamental problem in computer vision area with many real-world applications.

Anomaly Detection

Aurora Guard: Real-Time Face Anti-Spoofing via Light Reflection

no code implementations27 Feb 2019 Yao Liu, Ying Tai, Jilin Li, Shouhong Ding, Chengjie Wang, Feiyue Huang, Dongyang Li, Wenshuai Qi, Rongrong Ji

In this paper, we propose a light reflection based face anti-spoofing method named Aurora Guard (AG), which is fast, simple yet effective that has already been deployed in real-world systems serving for millions of users.

Face Anti-Spoofing General Classification

Learning deep representation from coarse to fine for face alignment

no code implementations31 Jul 2016 Zhiwen Shao, Shouhong Ding, Yiru Zhao, Qinchuan Zhang, Lizhuang Ma

In this paper, we propose a novel face alignment method that trains deep convolutional network from coarse to fine.

Face Alignment

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