3 papers
cs.LG2021
Towards A Conceptually Simple Defensive Approach for Few-shot classifiers Against Adversarial Support Samples
Yi Xiang Marcus Tan, Penny Chong, Jiamei Sun +3
Few-shot classifiers have been shown to exhibit promising results in use cases where user-provided labels are scarce. These models are able to learn to predict novel classes simply…
cs.CR2020
Detection of Adversarial Supports in Few-shot Classifiers Using Self-Similarity and Filtering
Yi Xiang Marcus Tan, Penny Chong, Jiamei Sun +3
Few-shot classifiers excel under limited training samples, making them useful in applications with sparsely user-provided labels. Their unique relative prediction setup offers oppo…
cs.LG2019
Exploring the Back Alleys: Analysing The Robustness of Alternative Neural Network Architectures against Adversarial Attacks
Yi Xiang Marcus Tan, Yuval Elovici, Alexander Binder
We investigate to what extent alternative variants of Artificial Neural Networks (ANNs) are susceptible to adversarial attacks. We analyse the adversarial robustness of conventiona…