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08/30/2023
MDTD: A Multi Domain Trojan Detector for Deep Neural Networks
Machine learning models that use deep neural networks (DNNs) are vulnera...
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12/03/2022
LDL: A Defense for Label-Based Membership Inference Attacks
The data used to train deep neural network (DNN) models in applications ...
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07/13/2022
Game of Trojans: A Submodular Byzantine Approach
Machine learning models in the wild have been shown to be vulnerable to ...
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03/25/2022
Trojan Horse Training for Breaking Defenses against Backdoor Attacks in Deep Learning
Machine learning (ML) models that use deep neural networks are vulnerabl...
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03/18/2022
Privacy-Preserving Reinforcement Learning Beyond Expectation
Cyber and cyber-physical systems equipped with machine learning algorith...
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11/18/2020
Adversarial Profiles: Detecting Out-Distribution Adversarial Samples in Pre-trained CNNs
Despite high accuracy of Convolutional Neural Networks (CNNs), they are ...
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05/17/2020
Toward Adversarial Robustness by Diversity in an Ensemble of Specialized Deep Neural Networks
We aim at demonstrating the influence of diversity in the ensemble of CN...
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10/18/2019
Toward Metrics for Differentiating Out-of-Distribution Sets
Vanilla CNNs, as uncalibrated classifiers, suffer from classifying out-o...
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08/21/2018
Controlling Over-generalization and its Effect on Adversarial Examples Generation and Detection
Convolutional Neural Networks (CNNs) allowed improving the state-of-the-...
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04/24/2018
