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09/21/2023
DeepTheft: Stealing DNN Model Architectures through Power Side Channel
Deep Neural Network (DNN) models are often deployed in resource-sharing ...
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07/31/2023
Text-CRS: A Generalized Certified Robustness Framework against Textual Adversarial Attacks
The language models, especially the basic text classification models, ha...
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04/05/2023
A Certified Radius-Guided Attack Framework to Image Segmentation Models
Image segmentation is an important problem in many safety-critical appli...
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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...
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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 ...
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07/05/2022
UniCR: Universally Approximated Certified Robustness via Randomized Smoothing
We study certified robustness of machine learning classifiers against ad...
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06/11/2022
NeuGuard: Lightweight Neuron-Guided Defense against Membership Inference Attacks
Membership inference attacks (MIAs) against machine learning models can ...
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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 ...
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03/14/2022
GraphTrack: A Graph-based Cross-Device Tracking Framework
Cross-device tracking has drawn growing attention from both commercial c...
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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...
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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 ...
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07/03/2021
Privacy-Preserving Representation Learning on Graphs: A Mutual Information Perspective
Learning with graphs has attracted significant attention recently. Exist...
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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-...
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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...
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12/08/2020
Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective
Federated learning (FL) is a popular distributed learning framework that...
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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...
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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...
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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...
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09/01/2020
Evasion Attacks to Graph Neural Networks via Influence Function
Graph neural networks (GNNs) have achieved state-of-the-art performance ...
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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...
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08/24/2020
Certified Robustness of Graph Neural Networks against Adversarial Structural Perturbation
Graph neural networks (GNNs) have recently gained much attention for nod...
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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...
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06/19/2020
Backdoor Attacks to Graph Neural Networks
Node classification and graph classification are two basic graph analyti...
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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...
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02/26/2020
On Certifying Robustness against Backdoor Attacks via Randomized Smoothing
Backdoor attack is a severe security threat to deep neural networks (DNN...
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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...
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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...
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03/01/2019
Attacking Graph-based Classification via Manipulating the Graph Structure
Graph-based classification methods are widely used for security and priv...
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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...
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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...
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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...
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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....
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02/14/2018
Stealing Hyperparameters in Machine Learning
Hyperparameters are critical in machine learning, as different hyperpara...
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01/27/2018
