b'Binghui Wang'

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Featured Co-authors

Lawrence Carin
178 publications
Ang Li
174 publications
Lei Yang
117 publications
Peng Gao
111 publications
Qi Li
109 publications
Xinyu Zhang
106 publications
Yiran Chen
100 publications
Pan Zhou
99 publications
Hai Li
85 publications
Surya Nepal
76 publications
Caiwen Ding
68 publications

research

∙ 09/21/2023

DeepTheft: Stealing DNN Model Architectures through Power Side Channel

Deep Neural Network (DNN) models are often deployed in resource-sharing ...

0 Yansong Gao, et al. ∙

research

∙ 07/31/2023

Text-CRS: A Generalized Certified Robustness Framework against Textual Adversarial Attacks

The language models, especially the basic text classification models, ha...

0 Xinyu Zhang, et al. ∙

research

∙ 04/05/2023

A Certified Radius-Guided Attack Framework to Image Segmentation Models

Image segmentation is an important problem in many safety-critical appli...

0 Wenjie Qu, et al. ∙

research

∙ 03/24/2023

IDGI: A Framework to Eliminate Explanation Noise from Integrated Gradients

Integrated Gradients (IG) as well as its variants are well-known techniq...

0 Ruo Yang, et al. ∙

research

∙ 03/10/2023

Turning Strengths into Weaknesses: A Certified Robustness Inspired Attack Framework against Graph Neural Networks

Graph neural networks (GNNs) have achieved state-of-the-art performance ...

0 Binghui Wang, et al. ∙

research

∙ 07/05/2022

UniCR: Universally Approximated Certified Robustness via Randomized Smoothing

We study certified robustness of machine learning classifiers against ad...

0 Hanbin Hong, et al. ∙

research

∙ 06/11/2022

NeuGuard: Lightweight Neuron-Guided Defense against Membership Inference Attacks

Membership inference attacks (MIAs) against machine learning models can ...

0 Nuo Xu, et al. ∙

research

∙ 05/07/2022

Bandits for Structure Perturbation-based Black-box Attacks to Graph Neural Networks with Theoretical Guarantees

Graph neural networks (GNNs) have achieved state-of-the-art performance ...

0 Binghui Wang, et al. ∙

research

∙ 03/14/2022

GraphTrack: A Graph-based Cross-Device Tracking Framework

Cross-device tracking has drawn growing attention from both commercial c...

0 Binghui Wang, et al. ∙

research

∙ 10/15/2021

Detecting Gender Bias in Transformer-based Models: A Case Study on BERT

In this paper, we propose a novel gender bias detection method by utiliz...

0 Bingbing Li, et al. ∙

research

∙ 08/21/2021

A Hard Label Black-box Adversarial Attack Against Graph Neural Networks

Graph Neural Networks (GNNs) have achieved state-of-the-art performance ...

0 Jiaming Mu, et al. ∙

research

∙ 07/03/2021

Privacy-Preserving Representation Learning on Graphs: A Mutual Information Perspective

Learning with graphs has attracted significant attention recently. Exist...

0 Binghui Wang, et al. ∙

research

∙ 04/22/2021

Towards Adversarial Patch Analysis and Certified Defense against Crowd Counting

Crowd counting has drawn much attention due to its importance in safety-...

0 Qiming Wu, et al. ∙

research

∙ 12/24/2020

Semi-Supervised Node Classification on Graphs: Markov Random Fields vs. Graph Neural Networks

Semi-supervised node classification on graph-structured data has many ap...

0 Binghui Wang, et al. ∙

research

∙ 12/08/2020

Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective

Federated learning (FL) is a popular distributed learning framework that...

0 Jingwei Sun, et al. ∙

research

∙ 12/08/2020

GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs

Graph-based semi-supervised node classification (GraphSSC) has wide appl...

0 Binghui Wang, et al. ∙

research

∙ 11/15/2020

Almost Tight L0-norm Certified Robustness of Top-k Predictions against Adversarial Perturbations

Top-k predictions are used in many real-world applications such as machi...

0 Jinyuan Jia, et al. ∙

research

∙ 10/26/2020

Robust and Verifiable Information Embedding Attacks to Deep Neural Networks via Error-Correcting Codes

In the era of deep learning, a user often leverages a third-party machin...

0 Jinyuan Jia, et al. ∙

research

∙ 09/01/2020

Evasion Attacks to Graph Neural Networks via Influence Function

Graph neural networks (GNNs) have achieved state-of-the-art performance ...

0 Binghui Wang, et al. ∙

research

∙ 09/01/2020

Reinforcement Learning-based Black-Box Evasion Attacks to Link Prediction in Dynamic Graphs

Link prediction in dynamic graphs (LPDG) is an important research proble...

4 Houxiang Fan, et al. ∙

research

∙ 08/24/2020

Certified Robustness of Graph Neural Networks against Adversarial Structural Perturbation

Graph neural networks (GNNs) have recently gained much attention for nod...

0 Binghui Wang, et al. ∙

research

∙ 08/07/2020

LotteryFL: Personalized and Communication-Efficient Federated Learning with Lottery Ticket Hypothesis on Non-IID Datasets

Federated learning is a popular distributed machine learning paradigm wi...

12 Ang Li, et al. ∙

research

∙ 06/19/2020

Backdoor Attacks to Graph Neural Networks

Node classification and graph classification are two basic graph analyti...

0 Zaixi Zhang, et al. ∙

research

∙ 04/29/2020

Perturbing Across the Feature Hierarchy to Improve Standard and Strict Blackbox Attack Transferability

We consider the blackbox transfer-based targeted adversarial attack thre...

0 Nathan Inkawhich, et al. ∙

research

∙ 02/26/2020

On Certifying Robustness against Backdoor Attacks via Randomized Smoothing

Backdoor attack is a severe security threat to deep neural networks (DNN...

0 Binghui Wang, et al. ∙

research

∙ 02/09/2020

Certified Robustness of Community Detection against Adversarial Structural Perturbation via Randomized Smoothing

Community detection plays a key role in understanding graph structure. H...

0 Jinyuan Jia, et al. ∙

research

∙ 12/20/2019

Certified Robustness for Top-k Predictions against Adversarial Perturbations via Randomized Smoothing

It is well-known that classifiers are vulnerable to adversarial perturba...

0 Jinyuan Jia, et al. ∙

research

∙ 03/01/2019

Attacking Graph-based Classification via Manipulating the Graph Structure

Graph-based classification methods are widely used for security and priv...

0 Binghui Wang, et al. ∙

research

∙ 12/04/2018

Graph-based Security and Privacy Analytics via Collective Classification with Joint Weight Learning and Propagation

Many security and privacy problems can be modeled as a graph classificat...

0 Binghui Wang, et al. ∙

research

∙ 06/13/2018

SybilBlind: Detecting Fake Users in Online Social Networks without Manual Labels

Detecting fake users (also called Sybils) in online social networks is a...

0 Binghui Wang, et al. ∙

research

∙ 03/19/2018

SybilFuse: Combining Local Attributes with Global Structure to Perform Robust Sybil Detection

Sybil attacks are becoming increasingly widespread and pose a significan...

0 Peng Gao, et al. ∙

research

∙ 03/12/2018

Structure-based Sybil Detection in Social Networks via Local Rule-based Propagation

Sybil detection in social networks is a basic security research problem....

0 Binghui Wang, et al. ∙

research

∙ 02/14/2018

Stealing Hyperparameters in Machine Learning

Hyperparameters are critical in machine learning, as different hyperpara...

0 Binghui Wang, et al. ∙

research

∙ 01/27/2018

Robust Multi-subspace Analysis Using Novel Column L0-norm Constrained Matrix Factorization

We study the underlying structure of data (approximately) generated from...

0 Binghui Wang, et al. ∙

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