Search Results for author:
Found 1 papers, 0 papers with code
Poisoning Attacks and Defenses on Artificial Intelligence: A Survey
This survey is conducted with a main intention of highlighting the most relevant information related to security vulnerabilities in the context of machine learning (ML) classifiers; more specifically, directed towards training procedures against data poisoning attacks, representing a type of attack that consists of tampering the data samples fed to the model during the training phase, leading to a degradation in the models accuracy during the inference phase.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
