485 citations · 1.5k across the 72 of their papers we have counts for
7 papers · 2 filters
-Robustness and Beyond: Unleashing Efficient Adversarial Training
Hadi M. Dolatabadi, Sarah Erfani, Christopher Leckie
Neural networks are vulnerable to adversarial attacks: adding well-crafted, imperceptible perturbations to their input can modify their output. Adversarial training is one of the m…
Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks
Hanxun Huang, Yisen Wang, Sarah Monazam Erfani +3
Deep neural networks (DNNs) are known to be vulnerable to adversarial attacks. A range of defense methods have been proposed to train adversarially robust DNNs, among which adversa…
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…
Neural Architecture Search via Combinatorial Multi-Armed Bandit
Hanxun Huang, Xingjun Ma, Sarah M. Erfani +1
Neural Architecture Search (NAS) has gained significant popularity as an effective tool for designing high performance deep neural networks (DNNs). NAS can be performed via policy…