6 papers
A unifying Bayesian framework for adversarial robustness
Pablo G. Arce, Roi Naveiro, David RÃos Insua
The vulnerability of machine learning models to adversarial attacks remains a critical societal security challenge. Traditional defenses, such as adversarial training, typically ro…
Simulation Based Bayesian Optimization
Roi Naveiro, Becky Tang
Bayesian Optimization (BO) is a powerful method for optimizing black-box functions by combining prior knowledge with ongoing function evaluations. BO constructs a probabilistic sur…
Protecting Classifiers From Attacks
Victor Gallego, Roi Naveiro, Alberto Redondo +2
In multiple domains such as malware detection, automated driving systems, or fraud detection, classification algorithms are susceptible to being attacked by malicious agents willin…
Evasion Attacks Against Bayesian Predictive Models
Pablo G. Arce, Roi Naveiro, David RÃos Insua
There is an increasing interest in analyzing the behavior of machine learning systems against adversarial attacks. However, most of the research in adversarial machine learning has…
Computational adversarial risk analysis for general security games
Jose Manuel Camacho, Roi Naveiro, David Rios Insua
This paper provides an efficient computational scheme to handle general security games from an adversarial risk analysis perspective. Two cases in relation to single-stage and mult…
Poisoning Bayesian Inference via Data Deletion and Replication
Matthieu Carreau, Roi Naveiro, William N. Caballero
Research in adversarial machine learning (AML) has shown that statistical models are vulnerable to maliciously altered data. However, despite advances in Bayesian machine learning…