125 citations · 223 across the 21 of their papers we have counts for
16 papers · 1 filter
COLLIDER: A Robust Training Framework for Backdoor Data
Hadi M. Dolatabadi, Sarah Erfani, Christopher Leckie
Deep neural network (DNN) classifiers are vulnerable to backdoor attacks. An adversary poisons some of the training data in such attacks by installing a trigger. The goal is to mak…
Double Bubble, Toil and Trouble: Enhancing Certified Robustness through Transitivity
Andrew C. Cullen, Paul Montague, Shijie Liu +2
In response to subtle adversarial examples flipping classifications of neural network models, recent research has promoted certified robustness as a solution. There, invariance of…
Local Intrinsic Dimensionality Signals Adversarial Perturbations
Sandamal Weerasinghe, Tansu Alpcan, Sarah M. Erfani +2
The vulnerability of machine learning models to adversarial perturbations has motivated a significant amount of research under the broad umbrella of adversarial machine learning. S…
Dual Head Adversarial Training
Yujing Jiang, Xingjun Ma, Sarah Monazam Erfani +1
Deep neural networks (DNNs) are known to be vulnerable to adversarial examples/attacks, raising concerns about their reliability in safety-critical applications. A number of defens…
Unlearnable Examples: Making Personal Data Unexploitable
Hanxun Huang, Xingjun Ma, Sarah Monazam Erfani +2
The volume of "free" data on the internet has been key to the current success of deep learning. However, it also raises privacy concerns about the unauthorized exploitation of pers…
A Deep Adversarial Model for Suffix and Remaining Time Prediction of Event Sequences
Farbod Taymouri, Marcello La Rosa, Sarah M. Erfani
Event suffix and remaining time prediction are sequence to sequence learning tasks. They have wide applications in different areas such as economics, digital health, business proce…