160 citations · 610 across the 19 of their papers we have counts for
5 papers · 1 filter
Universal Adversarial Robustness of Texture and Shape-Biased Models
Kenneth T. Co, Luis Muñoz-González, Leslie Kanthan +2
Increasing shape-bias in deep neural networks has been shown to improve robustness to common corruptions and noise. In this paper we analyze the adversarial robustness of texture a…
Byzantine-Robust Federated Machine Learning through Adaptive Model Averaging
Luis Muñoz-González, Kenneth T. Co, Emil C. Lupu
Federated learning enables training collaborative machine learning models at scale with many participants whilst preserving the privacy of their datasets. Standard federated learni…
Sensitivity of Deep Convolutional Networks to Gabor Noise
Kenneth T. Co, Luis Muñoz-González, Emil C. Lupu
Deep Convolutional Networks (DCNs) have been shown to be sensitive to Universal Adversarial Perturbations (UAPs): input-agnostic perturbations that fool a model on large portions o…
Poisoning Attacks with Generative Adversarial Nets
Luis Muñoz-González, Bjarne Pfitzner, Matteo Russo +2
Machine learning algorithms are vulnerable to poisoning attacks: An adversary can inject malicious points in the training dataset to influence the learning process and degrade the…
Efficient attack countermeasure selection accounting for recovery and action costs
Jukka Soikkeli, Luis Muñoz-González, Emil C. Lupu
The losses arising from a system being hit by cyber attacks can be staggeringly high, but defending against such attacks can also be costly. This work proposes an attack countermea…