58 citations · 305 across the 33 of their papers we have counts for
33 papers
BackdoorBox: A Python Toolbox for Backdoor Learning
Yiming Li, Mengxi Ya, Yang Bai +2
Third-party resources (, samples, backbones, and pre-trained models) are usually involved in the training of deep neural networks (DNNs), which brings backdoor attacks as a n…
Untargeted Backdoor Attack against Object Detection
Chengxiao Luo, Yiming Li, Yong Jiang +1
Recent studies revealed that deep neural networks (DNNs) are exposed to backdoor threats when training with third-party resources (such as training samples or backbones). The backd…
Backdoor Defense via Suppressing Model Shortcuts
Sheng Yang, Yiming Li, Yong Jiang +1
Recent studies have demonstrated that deep neural networks (DNNs) are vulnerable to backdoor attacks during the training process. Specifically, the adversaries intend to embed hidd…
BATT: Backdoor Attack with Transformation-based Triggers
Tong Xu, Yiming Li, Yong Jiang +1
Deep neural networks (DNNs) are vulnerable to backdoor attacks. The backdoor adversaries intend to maliciously control the predictions of attacked DNNs by injecting hidden backdoor…
Untargeted Backdoor Watermark: Towards Harmless and Stealthy Dataset Copyright Protection
Yiming Li, Yang Bai, Yong Jiang +3
Deep neural networks (DNNs) have demonstrated their superiority in practice. Arguably, the rapid development of DNNs is largely benefited from high-quality (open-sourced) datasets,…
Black-box Dataset Ownership Verification via Backdoor Watermarking
Yiming Li, Mingyan Zhu, Xue Yang +3
Deep learning, especially deep neural networks (DNNs), has been widely and successfully adopted in many critical applications for its high effectiveness and efficiency. The rapid d…
MOVE: Effective and Harmless Ownership Verification via Embedded External Features
Yiming Li, Linghui Zhu, Xiaojun Jia +5
Currently, deep neural networks (DNNs) are widely adopted in different applications. Despite its commercial values, training a well-performing DNN is resource-consuming. Accordingl…
Adaptive Frequency Learning in Two-branch Face Forgery Detection
Neng Wang, Yang Bai, Kun Yu +3
Face forgery has attracted increasing attention in recent applications of computer vision. Existing detection techniques using the two-branch framework benefit a lot from a frequen…
Few-Shot Backdoor Attacks on Visual Object Tracking
Yiming Li, Haoxiang Zhong, Xingjun Ma +2
Visual object tracking (VOT) has been widely adopted in mission-critical applications, such as autonomous driving and intelligent surveillance systems. In current practice, third-p…
Defending against Model Stealing via Verifying Embedded External Features
Yiming Li, Linghui Zhu, Xiaojun Jia +3
Obtaining a well-trained model involves expensive data collection and training procedures, therefore the model is a valuable intellectual property. Recent studies revealed that adv…
Clustering Effect of (Linearized) Adversarial Robust Models
Yang Bai, Xin Yan, Yong Jiang +2
Adversarial robustness has received increasing attention along with the study of adversarial examples. So far, existing works show that robust models not only obtain robustness aga…
Backdoor Attack in the Physical World
Yiming Li, Tongqing Zhai, Yong Jiang +2
Backdoor attack intends to inject hidden backdoor into the deep neural networks (DNNs), such that the prediction of infected models will be maliciously changed if the hidden backdo…
Hidden Backdoor Attack against Semantic Segmentation Models
Yiming Li, Yanjie Li, Yalei Lv +2
Deep neural networks (DNNs) are vulnerable to the \emph{backdoor attack}, which intends to embed hidden backdoors in DNNs by poisoning training data. The attacked model behaves nor…
Optimistic Dual Extrapolation for Coherent Non-monotone Variational Inequalities
Chaobing Song, Zhengyuan Zhou, Yichao Zhou +2
The optimization problems associated with training generative adversarial neural networks can be largely reduced to certain {\em non-monotone} variational inequality problems (VIPs…
Improving Adversarial Robustness via Channel-wise Activation Suppressing
Yang Bai, Yuyuan Zeng, Yong Jiang +3
The study of adversarial examples and their activation has attracted significant attention for secure and robust learning with deep neural networks (DNNs). Different from existing…
Open-sourced Dataset Protection via Backdoor Watermarking
Yiming Li, Ziqi Zhang, Jiawang Bai +3
The rapid development of deep learning has benefited from the release of some high-quality open-sourced datasets (, ImageNet), which allows researchers to easily verify the e…
Backdoor Attack against Speaker Verification
Tongqing Zhai, Yiming Li, Ziqi Zhang +3
Speaker verification has been widely and successfully adopted in many mission-critical areas for user identification. The training of speaker verification requires a large amount o…
Improving Query Efficiency of Black-box Adversarial Attack
Yang Bai, Yuyuan Zeng, Yong Jiang +3
Deep neural networks (DNNs) have demonstrated excellent performance on various tasks, however they are under the risk of adversarial examples that can be easily generated when the…
Rectified Decision Trees: Exploring the Landscape of Interpretable and Effective Machine Learning
Yiming Li, Jiawang Bai, Jiawei Li +3
Interpretability and effectiveness are two essential and indispensable requirements for adopting machine learning methods in reality. In this paper, we propose a knowledge distilla…
Neural Network-based Automatic Factor Construction
Jie Fang, Jianwu Lin, Shutao Xia +3
Instead of conducting manual factor construction based on traditional and behavioural finance analysis, academic researchers and quantitative investment managers have leveraged Gen…
Backdoor Learning: A Survey
Yiming Li, Yong Jiang, Zhifeng Li +1
Backdoor attack intends to embed hidden backdoor into deep neural networks (DNNs), so that the attacked models perform well on benign samples, whereas their predictions will be mal…
Variance Reduction via Accelerated Dual Averaging for Finite-Sum Optimization
Chaobing Song, Yong Jiang, Yi Ma
In this paper, we introduce a simplified and unified method for finite-sum convex optimization, named \emph{Variance Reduction via Accelerated Dual Averaging (VRADA)}. In both gene…
Breaking the Optimal Rate for a Class of Minimax Problems
Chaobing Song, Yong Jiang, Yi Ma
It is known that for convex optimization , the best possible rate of first order accelerated methods is . However, for th…
Toward Adversarial Robustness via Semi-supervised Robust Training
Yiming Li, Baoyuan Wu, Yan Feng +4
Adversarial examples have been shown to be the severe threat to deep neural networks (DNNs). One of the most effective adversarial defense methods is adversarial training (AT) thro…