Biblio
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2020. The Adversarial UFP/UFN Attack: A New Threat to ML-based Fake News Detection Systems? 2020 IEEE Symposium Series on Computational Intelligence (SSCI). :1523–1527.
In this paper, we propose two new attacks: the Adversarial Universal False Positive (UFP) Attack and the Adversarial Universal False Negative (UFN) Attack. The objective of this research is to introduce a new class of attack using only feature vector information. The results show the potential weaknesses of five machine learning (ML) classifiers. These classifiers include k-Nearest Neighbor (KNN), Naive Bayes (NB), Random Forrest (RF), a Support Vector Machine (SVM) with a Radial Basis Function (RBF) Kernel, and XGBoost (XGB).