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08/18/2023
Backdoor Mitigation by Correcting the Distribution of Neural Activations
Backdoor (Trojan) attacks are an important type of adversarial exploit a...
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08/08/2023
Improved Activation Clipping for Universal Backdoor Mitigation and Test-Time Detection
Deep neural networks are vulnerable to backdoor attacks (Trojans), where...
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05/13/2022
Universal Post-Training Backdoor Detection
A Backdoor attack (BA) is an important type of adversarial attack agains...
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01/20/2022
Post-Training Detection of Backdoor Attacks for Two-Class and Multi-Attack Scenarios
Backdoor attacks (BAs) are an emerging threat to deep neural network cla...
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12/06/2021
Test-Time Detection of Backdoor Triggers for Poisoned Deep Neural Networks
Backdoor (Trojan) attacks are emerging threats against deep neural netwo...
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10/20/2021
Detecting Backdoor Attacks Against Point Cloud Classifiers
Backdoor attacks (BA) are an emerging threat to deep neural network clas...
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09/06/2021
Backdoor Attack and Defense for Deep Regression
We demonstrate a backdoor attack on a deep neural network used for regre...
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07/28/2021
Robust and Active Learning for Deep Neural Network Regression
We describe a gradient-based method to discover local error maximizers o...
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05/28/2021
A BIC based Mixture Model Defense against Data Poisoning Attacks on Classifiers
Data Poisoning (DP) is an effective attack that causes trained classifie...
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05/21/2021
Anomaly Detection of Test-Time Evasion Attacks using Class-conditional Generative Adversarial Networks
Deep Neural Networks (DNNs) have been shown vulnerable to adversarial (T...
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04/12/2021
A Backdoor Attack against 3D Point Cloud Classifiers
Vulnerability of 3D point cloud (PC) classifiers has become a grave conc...
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10/20/2020
L-RED: Efficient Post-Training Detection of Imperceptible Backdoor Attacks without Access to the Training Set
Backdoor attacks (BAs) are an emerging form of adversarial attack typica...
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10/15/2020
Reverse Engineering Imperceptible Backdoor Attacks on Deep Neural Networks for Detection and Training Set Cleansing
Backdoor data poisoning is an emerging form of adversarial attack usuall...
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11/18/2019
Revealing Perceptible Backdoors, without the Training Set, via the Maximum Achievable Misclassification Fraction Statistic
Recently, a special type of data poisoning (DP) attack, known as a backd...
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10/15/2019
Notes on Lipschitz Margin, Lipschitz Margin Training, and Lipschitz Margin p-Values for Deep Neural Network Classifiers
We provide a local class purity theorem for Lipschitz continuous, half-r...
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08/27/2019
Revealing Backdoors, Post-Training, in DNN Classifiers via Novel Inference on Optimized Perturbations Inducing Group Misclassification
Recently, a special type of data poisoning (DP) attack targeting Deep Ne...
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04/12/2019
Adversarial Learning in Statistical Classification: A Comprehensive Review of Defenses Against Attacks
With the wide deployment of machine learning (ML) based systems for a va...
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10/31/2018
A Mixture Model Based Defense for Data Poisoning Attacks Against Naive Bayes Spam Filters
Naive Bayes spam filters are highly susceptible to data poisoning attack...
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10/31/2018
When Not to Classify: Detection of Reverse Engineering Attacks on DNN Image Classifiers
This paper addresses detection of a reverse engineering (RE) attack targ...
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12/18/2017
When Not to Classify: Anomaly Detection of Attacks (ADA) on DNN Classifiers at Test Time
A significant threat to the recent, wide deployment of machine learning-...
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12/20/2015
ATD: Anomalous Topic Discovery in High Dimensional Discrete Data
We propose an algorithm for detecting patterns exhibited by anomalous cl...
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06/28/2014
Convex Analysis of Mixtures for Separating Non-negative Well-grounded Sources
Blind Source Separation (BSS) has proven to be a powerful tool for the a...
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01/22/2014
