28 citations · 42 across the 6 of their papers we have counts for
8 papers · 1 filter
Hyperparameter Learning under Data Poisoning: Analysis of the Influence of Regularization via Multiobjective Bilevel Optimization
Javier Carnerero-Cano, Luis Muñoz-González, Phillippa Spencer +1
Machine Learning (ML) algorithms are vulnerable to poisoning attacks, where a fraction of the training data is manipulated to deliberately degrade the algorithms' performance. Opti…
Regularization Can Help Mitigate Poisoning Attacks... with the Right Hyperparameters
Javier Carnerero-Cano, Luis Muñoz-González, Phillippa Spencer +1
Machine learning algorithms are vulnerable to poisoning attacks, where a fraction of the training data is manipulated to degrade the algorithms' performance. We show that current a…
Real-time Detection of Practical Universal Adversarial Perturbations
Kenneth T. Co, Luis Muñoz-González, Leslie Kanthan +1
Universal Adversarial Perturbations (UAPs) are a prominent class of adversarial examples that exploit the systemic vulnerabilities and enable physically realizable and robust attac…
Robustness and Transferability of Universal Attacks on Compressed Models
Alberto G. Matachana, Kenneth T. Co, Luis Muñoz-González +2
Neural network compression methods like pruning and quantization are very effective at efficiently deploying Deep Neural Networks (DNNs) on edge devices. However, DNNs remain vulne…
Regularisation Can Mitigate Poisoning Attacks: A Novel Analysis Based on Multiobjective Bilevel Optimisation
Javier Carnerero-Cano, Luis Muñoz-González, Phillippa Spencer +1
Machine Learning (ML) algorithms are vulnerable to poisoning attacks, where a fraction of the training data is manipulated to deliberately degrade the algorithms' performance. Opti…
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…