research
∙
08/03/2023
URET: Universal Robustness Evaluation Toolkit (for Evasion)
Machine learning models are known to be vulnerable to adversarial evasio...
share
research
∙
12/14/2020
Adaptive Verifiable Training Using Pairwise Class Similarity
Verifiable training has shown success in creating neural networks that a...
research
∙
07/14/2020
Adversarial Examples and Metrics
Adversarial examples are a type of attack on machine learning (ML) syste...
research
∙
06/11/2020
A new measure for overfitting and its implications for backdooring of deep learning
Overfitting describes the phenomenon that a machine learning model fits ...
research
∙
12/07/2018
Reaching Data Confidentiality and Model Accountability on the CalTrain
Distributed collaborative learning (DCL) paradigms enable building joint...
research
∙
11/09/2018
Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering
While machine learning (ML) models are being increasingly trusted to mak...
research
∙
07/03/2018
Securing Input Data of Deep Learning Inference Systems via Partitioned Enclave Execution
Deep learning systems have been widely deployed as backend engines of ar...
research
∙
05/31/2018
Defending Against Model Stealing Attacks Using Deceptive Perturbations
Machine learning models are vulnerable to simple model stealing attacks ...
research
∙
12/11/2017
IDIoT: Securing the Internet of Things like it's 1994
Over 20 billion Internet of Things devices are set to come online by 202...
research
∙
11/12/2013