42 citations · 207 across the 76 of their papers we have counts for
19 papers · 1 filter
A Novel Latent-Class Attack and its Detection by Class Subspace Orthogonalization
Guangmingmei Yang, David J. Miller, George Kesidis
Deep learning, which in general relies on voluminous amounts of training data, is vulnerable to data poisoning attacks, including error-generic attacks and backdoors (Trojans). In…
Improving the Sensitivity of Backdoor Detectors via Class Subspace Orthogonalization
Guangmingmei Yang, David J. Miller, George Kesidis
Most post-training backdoor detection methods rely on attacked models exhibiting extreme outlier detection statistics for the target class of an attack, compared to non-target clas…
Inverting Trojans in LLMs
Zhengxing Li, Guangmingmei Yang, Jayaram Raghuram +2
While effective backdoor detection and inversion schemes have been developed for AIs used e.g. for images, there are challenges in "porting" these methods to LLMs. First, the LLM i…
Post-Training Overfitting Mitigation in DNN Classifiers
Hang Wang, David J. Miller, George Kesidis
Well-known (non-malicious) sources of overfitting in deep neural net (DNN) classifiers include: i) large class imbalances; ii) insufficient training-set diversity; and iii) over-tr…
Backdoor Mitigation by Correcting the Distribution of Neural Activations
Xi Li, Zhen Xiang, David J. Miller +1
Backdoor (Trojan) attacks are an important type of adversarial exploit against deep neural networks (DNNs), wherein a test instance is (mis)classified to the attacker's target clas…
Improved Activation Clipping for Universal Backdoor Mitigation and Test-Time Detection
Hang Wang, Zhen Xiang, David J. Miller +1
Deep neural networks are vulnerable to backdoor attacks (Trojans), where an attacker poisons the training set with backdoor triggers so that the neural network learns to classify t…
Training set cleansing of backdoor poisoning by self-supervised representation learning
H. Wang, S. Karami, O. Dia +6
A backdoor or Trojan attack is an important type of data poisoning attack against deep neural network (DNN) classifiers, wherein the training dataset is poisoned with a small numbe…
Analysis of Distributed Deep Learning in the Cloud
Aakash Sharma, Vivek M. Bhasi, Sonali Singh +6
We aim to resolve this problem by introducing a comprehensive distributed deep learning (DDL) profiler, which can determine the various execution "stalls" that DDL suffers from whi…
MM-BD: Post-Training Detection of Backdoor Attacks with Arbitrary Backdoor Pattern Types Using a Maximum Margin Statistic
Hang Wang, Zhen Xiang, David J. Miller +1
Backdoor attacks are an important type of adversarial threat against deep neural network classifiers, wherein test samples from one or more source classes will be (mis)classified t…
Backdoor Attack and Defense for Deep Regression
Xi Li, George Kesidis, David J. Miller +1
We demonstrate a backdoor attack on a deep neural network used for regression. The backdoor attack is localized based on training-set data poisoning wherein the mislabeled samples…
Robust and Active Learning for Deep Neural Network Regression
Xi Li, George Kesidis, David J. Miller +3
We describe a gradient-based method to discover local error maximizers of a deep neural network (DNN) used for regression, assuming the availability of an "oracle" capable of provi…
A BIC-based Mixture Model Defense against Data Poisoning Attacks on Classifiers
Xi Li, David J. Miller, Zhen Xiang +1
Data Poisoning (DP) is an effective attack that causes trained classifiers to misclassify their inputs. DP attacks significantly degrade a classifier's accuracy by covertly injecti…
Anomaly Detection of Adversarial Examples using Class-conditional Generative Adversarial Networks
Hang Wang, David J. Miller, George Kesidis
Deep Neural Networks (DNNs) have been shown vulnerable to Test-Time Evasion attacks (TTEs, or adversarial examples), which, by making small changes to the input, alter the DNN's de…
Reverse Engineering Imperceptible Backdoor Attacks on Deep Neural Networks for Detection and Training Set Cleansing
Zhen Xiang, David J. Miller, George Kesidis
Backdoor data poisoning is an emerging form of adversarial attack usually against deep neural network image classifiers. The attacker poisons the training set with a relatively sma…
Revealing Perceptible Backdoors, without the Training Set, via the Maximum Achievable Misclassification Fraction Statistic
Zhen Xiang, David J. Miller, Hang Wang +1
Recently, a backdoor data poisoning attack was proposed, which adds mislabeled examples to the training set, with an embedded backdoor pattern, aiming to have the classifier learn…
Detection of Backdoors in Trained Classifiers Without Access to the Training Set
Zhen Xiang, David J. Miller, George Kesidis
Recently, a special type of data poisoning (DP) attack targeting Deep Neural Network (DNN) classifiers, known as a backdoor, was proposed. These attacks do not seek to degrade clas…
Adversarial Learning in Statistical Classification: A Comprehensive Review of Defenses Against Attacks
David J. Miller, Zhen Xiang, George Kesidis
There is great potential for damage from adversarial learning (AL) attacks on machine-learning based systems. In this paper, we provide a contemporary survey of AL, focused particu…
When Not to Classify: Anomaly Detection of Attacks (ADA) on DNN Classifiers at Test Time
David J. Miller, Yulia Wang, George Kesidis
A significant threat to the recent, wide deployment of machine learning-based systems, including deep neural networks (DNNs), is adversarial learning attacks. We analyze possible t…
A Study of Unsupervised Adaptive Crowdsourcing
G. Kesidis, A. Kurve
We consider unsupervised crowdsourcing performance based on the model wherein the responses of end-users are essentially rated according to how their responses correlate with the m…