1 code implementation • ECCV 2020 • Yanbo Fan, Baoyuan Wu, Tuanhui Li, Yong Zhang, Mingyang Li, Zhifeng Li, Yujiu Yang
Based on this factorization, we formulate the sparse attack problem as a mixed integer programming (MIP) to jointly optimize the binary selection factors and continuous perturbation magnitudes of all pixels, with a cardinality constraint on selection factors to explicitly control the degree of sparsity.
no code implementations • 18 Mar 2024 • Yuting Xiao, Xuan Wang, Jiafei Li, Hongrui Cai, Yanbo Fan, Nan Xue, Minghui Yang, Yujun Shen, Shenghua Gao
To this end, we propose a novel approach, GauMesh, to bridge the 3D Gaussian and Mesh for modeling and rendering the dynamic scenes.
no code implementations • 18 Dec 2023 • Hao Hu, Xuan Wang, Jingxiang Sun, Yanbo Fan, Yu Guo, Caigui Jiang
To address these, we propose a novel scalable vector graphic reconstruction and animation method, dubbed VectorTalker.
no code implementations • ICCV 2023 • Yuan Gong, Yong Zhang, Xiaodong Cun, Fei Yin, Yanbo Fan, Xuan Wang, Baoyuan Wu, Yujiu Yang
Moreover, since no paired data is provided, we propose a novel cross-domain training scheme using data from two domains with the designed analogy constraint.
no code implementations • 14 Jul 2023 • Zihao Zhu, Mingda Zhang, Shaokui Wei, Li Shen, Yanbo Fan, Baoyuan Wu
To further integrate it with normal training process, we then propose a learnable poisoning sample selection strategy to learn the mask together with the model parameters through a min-max optimization. Specifically, the outer loop aims to achieve the backdoor attack goal by minimizing the loss based on the selected samples, while the inner loop selects hard poisoning samples that impede this goal by maximizing the loss.
no code implementations • 7 Jul 2023 • Wangbo Yu, Yanbo Fan, Yong Zhang, Xuan Wang, Fei Yin, Yunpeng Bai, Yan-Pei Cao, Ying Shan, Yang Wu, Zhongqian Sun, Baoyuan Wu
In this work, we propose a one-shot 3D facial avatar reconstruction framework that only requires a single source image to reconstruct a high-fidelity 3D facial avatar.
1 code implementation • 6 Jul 2023 • Xu Han, Anmin Liu, Chenxuan Yao, Yanbo Fan, Kun He
In either case, the common gradient-based methods generally use the sign function to generate perturbations on the gradient update, that offers a roughly correct direction and has gained great success.
no code implementations • 1 Jun 2023 • Ruotong Wang, Hongrui Chen, Zihao Zhu, Li Liu, Yong Zhang, Yanbo Fan, Baoyuan Wu
We hope that the proposed VSSC trigger and implementation approach could inspire future studies on designing more practical triggers in backdoor attacks.
1 code implementation • ICCV 2023 • Zhiyuan Yan, Yong Zhang, Yanbo Fan, Baoyuan Wu
Deepfake detection remains a challenging task due to the difficulty of generalizing to new types of forgeries.
no code implementations • ICCV 2023 • Mingli Zhu, Shaokui Wei, Li Shen, Yanbo Fan, Baoyuan Wu
Fine-tuning based on benign data is a natural defense to erase the backdoor effect in a backdoored model.
1 code implementation • CVPR 2023 • Youxin Pang, Yong Zhang, Weize Quan, Yanbo Fan, Xiaodong Cun, Ying Shan, Dong-Ming Yan
In this paper, we introduce a novel self-supervised disentanglement framework to decouple pose and expression without 3DMMs and paired data, which consists of a motion editing module, a pose generator, and an expression generator.
1 code implementation • 1 Jan 2023 • Fei Yin, Yong Zhang, Baoyuan Wu, Yan Feng, Jingyi Zhang, Yanbo Fan, Yujiu Yang
In the scenario of black-box adversarial attack, the target model's parameters are unknown, and the attacker aims to find a successful adversarial perturbation based on query feedback under a query budget.
no code implementations • CVPR 2023 • Fei Yin, Yong Zhang, Xuan Wang, Tengfei Wang, Xiaoyu Li, Yuan Gong, Yanbo Fan, Xiaodong Cun, Ying Shan, Cengiz Oztireli, Yujiu Yang
It is natural to associate 3D GANs with GAN inversion methods to project a real image into the generator's latent space, allowing free-view consistent synthesis and editing, referred as 3D GAN inversion.
no code implementations • CVPR 2023 • Yunpeng Bai, Yanbo Fan, Xuan Wang, Yong Zhang, Jingxiang Sun, Chun Yuan, Ying Shan
Compared with existing works, we obtain superior novel view synthesis results and faithfully face reenactment performance.
1 code implementation • 27 Nov 2022 • Jiancong Xiao, Yanbo Fan, Ruoyu Sun, Zhi-Quan Luo
Specifically, we provide the first bound of adversarial Rademacher complexity of deep neural networks.
3 code implementations • 12 Oct 2022 • Zeyu Qin, Yanbo Fan, Yi Liu, Li Shen, Yong Zhang, Jue Wang, Baoyuan Wu
Furthermore, RAP can be naturally combined with many existing black-box attack techniques, to further boost the transferability.
1 code implementation • 3 Oct 2022 • Jiancong Xiao, Yanbo Fan, Ruoyu Sun, Jue Wang, Zhi-Quan Luo
In adversarial machine learning, deep neural networks can fit the adversarial examples on the training dataset but have poor generalization ability on the test set.
1 code implementation • 2 Oct 2022 • Jiancong Xiao, Liusha Yang, Yanbo Fan, Jue Wang, Zhi-Quan Luo
On synthetic datasets, theoretically, We prove that on-manifold adversarial examples are powerful, yet adversarial training focuses on off-manifold directions and ignores the on-manifold adversarial examples.
1 code implementation • 2 Oct 2022 • Jiancong Xiao, Zeyu Qin, Yanbo Fan, Baoyuan Wu, Jue Wang, Zhi-Quan Luo
Therefore, adversarial training for multiple perturbations (ATMP) is proposed to generalize the adversarial robustness over different perturbation types (in $\ell_1$, $\ell_2$, and $\ell_\infty$ norm-bounded perturbations).
no code implementations • 16 Sep 2022 • Siyuan Liang, Longkang Li, Yanbo Fan, Xiaojun Jia, Jingzhi Li, Baoyuan Wu, Xiaochun Cao
Recent studies have shown that detectors based on deep models are vulnerable to adversarial examples, even in the black-box scenario where the attacker cannot access the model information.
1 code implementation • 28 Aug 2022 • Mingdeng Cao, Zhihang Zhong, Yanbo Fan, Jiahao Wang, Yong Zhang, Jue Wang, Yujiu Yang, Yinqiang Zheng
We believe the novel realistic synthesis pipeline and the corresponding RAW video dataset can help the community to easily construct customized blur datasets to improve real-world video deblurring performance largely, instead of laboriously collecting real data pairs.
1 code implementation • 14 Aug 2022 • Chengyin Xu, Zenghao Chai, Zhengzhuo Xu, Chun Yuan, Yanbo Fan, Jue Wang
Image retrieval has become an increasingly appealing technique with broad multimedia application prospects, where deep hashing serves as the dominant branch towards low storage and efficient retrieval.
no code implementations • 6 Jun 2022 • Zhichao Huang, Yanbo Fan, Chen Liu, Weizhong Zhang, Yong Zhang, Mathieu Salzmann, Sabine Süsstrunk, Jue Wang
While adversarial training and its variants have shown to be the most effective algorithms to defend against adversarial attacks, their extremely slow training process makes it hard to scale to large datasets like ImageNet.
no code implementations • 24 May 2022 • Yunpeng Bai, Cairong Wang, Chun Yuan, Yanbo Fan, Jue Wang
The content contrastive loss enables the encoder to retain more available details.
1 code implementation • 17 Apr 2022 • Mingdeng Cao, Yanbo Fan, Yong Zhang, Jue Wang, Yujiu Yang
For multi-frame temporal modeling, we adapt Transformer to fuse multiple spatial features efficiently.
1 code implementation • 6 Apr 2022 • Xu Han, Anmin Liu, Yifeng Xiong, Yanbo Fan, Kun He
Deviation between the original gradient and the generated noises may lead to inaccurate gradient update estimation and suboptimal solutions for adversarial transferability, which is crucial for black-box attacks.
1 code implementation • 8 Mar 2022 • Fei Yin, Yong Zhang, Xiaodong Cun, Mingdeng Cao, Yanbo Fan, Xuan Wang, Qingyan Bai, Baoyuan Wu, Jue Wang, Yujiu Yang
Our framework elevates the resolution of the synthesized talking face to 1024*1024 for the first time, even though the training dataset has a lower resolution.
no code implementations • ICCV 2021 • Siyuan Liang, Baoyuan Wu, Yanbo Fan, Xingxing Wei, Xiaochun Cao
Extensive experiments demonstrate that our method can effectively and efficiently attack various popular object detectors, including anchor-based and anchor-free, and generate transferable adversarial examples.
no code implementations • 20 Sep 2021 • Xin Zheng, Yanbo Fan, Baoyuan Wu, Yong Zhang, Jue Wang, Shirui Pan
Face recognition has been greatly facilitated by the development of deep neural networks (DNNs) and has been widely applied to many safety-critical applications.
1 code implementation • CVPR 2022 • Tengfei Wang, Yong Zhang, Yanbo Fan, Jue Wang, Qifeng Chen
With a low bit-rate latent code, previous works have difficulties in preserving high-fidelity details in reconstructed and edited images.
no code implementations • 2 Sep 2021 • Chuanbiao Song, Yanbo Fan, Yichen Yang, Baoyuan Wu, Yiming Li, Zhifeng Li, Kun He
Adversarial training (AT) has been demonstrated as one of the most promising defense methods against various adversarial attacks.
1 code implementation • ICCV 2021 • Shulan Ruan, Yong Zhang, Kun Zhang, Yanbo Fan, Fan Tang, Qi Liu, Enhong Chen
Text-to-image synthesis refers to generating an image from a given text description, the key goal of which lies in photo realism and semantic consistency.
1 code implementation • NeurIPS 2021 • Zeyu Qin, Yanbo Fan, Hongyuan Zha, Baoyuan Wu
We conduct the theoretical analysis about the effectiveness of RND against query-based black-box attacks and the corresponding adaptive attacks.
no code implementations • 9 Nov 2020 • Jingyi Zhang, Yong Zhang, Baoyuan Wu, Yanbo Fan, Fumin Shen, Heng Tao Shen
We propose to incorporate the prior about the co-occurrence of relation pairs into the graph to further help alleviate the class imbalance issue.
1 code implementation • CVPR 2022 • Yan Feng, Baoyuan Wu, Yanbo Fan, Li Liu, Zhifeng Li, Shutao Xia
This work studies black-box adversarial attacks against deep neural networks (DNNs), where the attacker can only access the query feedback returned by the attacked DNN model, while other information such as model parameters or the training datasets are unknown.
no code implementations • 12 May 2020 • Chengcheng Ma, Baoyuan Wu, Shibiao Xu, Yanbo Fan, Yong Zhang, Xiaopeng Zhang, Zhifeng Li
In this work, we study the detection of adversarial examples, based on the assumption that the output and internal responses of one DNN model for both adversarial and benign examples follow the generalized Gaussian distribution (GGD), but with different parameters (i. e., shape factor, mean, and variance).
1 code implementation • 16 Mar 2020 • Yiming Li, Baoyuan Wu, Yan Feng, Yanbo Fan, Yong Jiang, Zhifeng Li, Shu-Tao Xia
In this work, we propose a novel defense method, the robust training (RT), by jointly minimizing two separated risks ($R_{stand}$ and $R_{rob}$), which is with respect to the benign example and its neighborhoods respectively.
no code implementations • 26 Feb 2020 • Yong Zhang, Le Li, Zhilei Liu, Baoyuan Wu, Yanbo Fan, Zhifeng Li
Most of the existing methods train models for one-versus-one kin relation, which only consider one parent face and one child face by directly using an auto-encoder without any explicit control over the resemblance of the synthesized face to the parent face.
no code implementations • IEEE Access ( Volume: 8 ) 2020 • Yanbo Fan, Shuchen Weng, Yong Zhang, Boxin Shi, Yi Zhang
To facilitate end-to-end training, we further develop a scenario context information extraction branch to extract context information from raw RGB video directly.
Ranked #82 on Skeleton Based Action Recognition on NTU RGB+D
1 code implementation • CVPR 2019 • Yan Xu, Baoyuan Wu, Fumin Shen, Yanbo Fan, Yong Zhang, Heng Tao Shen, Wei Liu
Due to the sequential dependencies among words in a caption, we formulate the generation of adversarial noises for targeted partial captions as a structured output learning problem with latent variables.
1 code implementation • 7 Jan 2019 • Baoyuan Wu, Weidong Chen, Yanbo Fan, Yong Zhang, Jinlong Hou, Jie Liu, Tong Zhang
In this work, we propose to train CNNs from images annotated with multiple tags, to enhance the quality of visual representation of the trained CNN model.
no code implementations • NeurIPS 2017 • Yanbo Fan, Siwei Lyu, Yiming Ying, Bao-Gang Hu
We further give a learning theory analysis of \matk learning on the classification calibration of the \atk loss and the error bounds of \atk-SVM.
no code implementations • 22 May 2017 • Yanbo Fan, Jian Liang, Ran He, Bao-Gang Hu, Siwei Lyu
In multi-view clustering, different views may have different confidence levels when learning a consensus representation.
no code implementations • 1 Jun 2016 • Yanbo Fan, Ran He, Jian Liang, Bao-Gang Hu
In this paper, we focus on the minimizer function, and study a group of new regularizer, named self-paced implicit regularizer that is deduced from robust loss function.