26 citations · 87 across the 12 of their papers we have counts for
12 papers
Surrogate Model Extension (SME): A Fast and Accurate Weight Update Attack on Federated Learning
Junyi Zhu, Ruicong Yao, Matthew B. Blaschko
In Federated Learning (FL) and many other distributed training frameworks, collaborators can hold their private data locally and only share the network weights trained with the loc…
Confidence-aware Personalized Federated Learning via Variational Expectation Maximization
Junyi Zhu, Xingchen Ma, Matthew B. Blaschko
Federated Learning (FL) is a distributed learning scheme to train a shared model across clients. One common and fundamental challenge in FL is that the sets of data across clients…
Learning Sample Difficulty from Pre-trained Models for Reliable Prediction
Peng Cui, Dan Zhang, Zhijie Deng +2
Large-scale pre-trained models have achieved remarkable success in many applications, but how to leverage them to improve the prediction reliability of downstream models is undesir…
Rethinking Model Ensemble in Transfer-based Adversarial Attacks
Huanran Chen, Yichi Zhang, Yinpeng Dong +3
It is widely recognized that deep learning models lack robustness to adversarial examples. An intriguing property of adversarial examples is that they can transfer across different…
A Comprehensive Study on Robustness of Image Classification Models: Benchmarking and Rethinking
Chang Liu, Yinpeng Dong, Wenzhao Xiang +7
The robustness of deep neural networks is usually lacking under adversarial examples, common corruptions, and distribution shifts, which becomes an important research problem in th…
Isometric 3D Adversarial Examples in the Physical World
Yibo Miao, Yinpeng Dong, Jun Zhu +1
3D deep learning models are shown to be as vulnerable to adversarial examples as 2D models. However, existing attack methods are still far from stealthy and suffer from severe perf…
ViewFool: Evaluating the Robustness of Visual Recognition to Adversarial Viewpoints
Yinpeng Dong, Shouwei Ruan, Hang Su +3
Recent studies have demonstrated that visual recognition models lack robustness to distribution shift. However, current work mainly considers model robustness to 2D image transform…
BadDet: Backdoor Attacks on Object Detection
Shih-Han Chan, Yinpeng Dong, Jun Zhu +2
Deep learning models have been deployed in numerous real-world applications such as autonomous driving and surveillance. However, these models are vulnerable in adversarial environ…
Controllable Evaluation and Generation of Physical Adversarial Patch on Face Recognition
Xiao Yang, Yinpeng Dong, Tianyu Pang +3
Recent studies have revealed the vulnerability of face recognition models against physical adversarial patches, which raises security concerns about the deployed face recognition s…
Robustness and Accuracy Could Be Reconcilable by (Proper) Definition
Tianyu Pang, Min Lin, Xiao Yang +2
The trade-off between robustness and accuracy has been widely studied in the adversarial literature. Although still controversial, the prevailing view is that this trade-off is inh…
Improving Differentially Private SGD via Randomly Sparsified Gradients
Junyi Zhu, Matthew B. Blaschko
Differentially private stochastic gradient descent (DP-SGD) has been widely adopted in deep learning to provide rigorously defined privacy, which requires gradient clipping to boun…
R-GAP: Recursive Gradient Attack on Privacy
Junyi Zhu, Matthew Blaschko
Federated learning frameworks have been regarded as a promising approach to break the dilemma between demands on privacy and the promise of learning from large collections of distr…