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05/01/2023
Deep Graph Representation Learning and Optimization for Influence Maximization
Influence maximization (IM) is formulated as selecting a set of initial ...
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02/16/2023
Towards Fair Machine Learning Software: Understanding and Addressing Model Bias Through Counterfactual Thinking
The increasing use of Machine Learning (ML) software can lead to unfair ...
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04/11/2022
Narcissus: A Practical Clean-Label Backdoor Attack with Limited Information
Backdoor attacks insert malicious data into a training set so that, duri...
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01/20/2022
Watermarking Pre-trained Encoders in Contrastive Learning
Contrastive learning has become a popular technique to pre-train image e...
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12/13/2020
DeepSweep: An Evaluation Framework for Mitigating DNN Backdoor Attacks using Data Augmentation
Public resources and services (e.g., datasets, training platforms, pre-t...
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12/03/2020
FenceBox: A Platform for Defeating Adversarial Examples with Data Augmentation Techniques
It is extensively studied that Deep Neural Networks (DNNs) are vulnerabl...
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07/30/2020
A Data Augmentation-based Defense Method Against Adversarial Attacks in Neural Networks
Deep Neural Networks (DNNs) in Computer Vision (CV) are well-known to be...
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05/27/2020
Mitigating Advanced Adversarial Attacks with More Advanced Gradient Obfuscation Techniques
Deep Neural Networks (DNNs) are well-known to be vulnerable to Adversari...
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04/17/2019
Privacy-preserving Health Data Sharing for Medical Cyber-Physical Systems
The recent spades of cyber security attacks have compromised end users' ...
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02/09/2018
