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Found 4 papers, 0 papers with code
Towards A Conceptually Simple Defensive Approach for Few-shot classifiers Against Adversarial Support Samples
In this work, we aim to close this gap by studying a conceptually simple approach to defend few-shot classifiers against adversarial attacks.
Detection of Adversarial Supports in Few-shot Classifiers Using Self-Similarity and Filtering
In this work, we propose a detection strategy to identify adversarial support sets, aimed at destroying the understanding of a few-shot classifier for a certain class.
Exploring the Back Alleys: Analysing The Robustness of Alternative Neural Network Architectures against Adversarial Attacks
We investigate to what extent alternative variants of Artificial Neural Networks (ANNs) are susceptible to adversarial attacks.
Adversarial Attacks on Remote User Authentication Using Behavioural Mouse Dynamics
In an attempt to address this gap, we built a set of attacks, which are applications of several generative approaches, to construct adversarial mouse trajectories that bypass authentication models.
