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Found 4 papers, 2 papers with code
Managing ML-Based Application Non-Functional Behavior: A Multi-Model Approach
Our solution goes beyond the state of the art by providing an architectural and methodological approach that continuously guarantees a stable non-functional behavior of ML-based applications, is applicable to different ML models, and is driven by non-functional properties assessed on the models themselves.
Rethinking Certification for Trustworthy Machine Learning-Based Applications
Machine Learning (ML) is increasingly used to implement advanced applications with non-deterministic behavior, which operate on the cloud-edge continuum.
Explainable Data Poison Attacks on Human Emotion Evaluation Systems based on EEG Signals
The major aim of this paper is to explain the data poisoning attacks using label-flipping during the training stage of the electroencephalogram (EEG) signal-based human emotion evaluation systems deploying Machine Learning models from the attackers' perspective.
On the Robustness of Random Forest Against Untargeted Data Poisoning: An Ensemble-Based Approach
This huge progress in terms of prediction quality does not however find a counterpart in the security of such models and corresponding predictions, where perturbations of fractions of the training set (poisoning) can seriously undermine the model accuracy.
