Behavior of Machine Learning Algorithms in Adversarial Environments

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Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering (2018, methodology): “Prior work (Barreno et al., 2010; Nelson, 2010; Huang et al., 2011; Papernot et al., 2016) has shown that this type of attack, called a poisoning attack , can lead to poor model performance, models that are easily fooled, and targeted misclassiﬁcations, exposing safety risks.”
Adversarial Robustness Toolbox v0.2.2 (2018, unknown): “versaries have the opportunity of manipulating the training data to signiﬁcantly decrease overall performance, cause targeted misclassiﬁcation or bad behavior, and insert backdoors and neural trojans [5,43,28,25,33,34,7,42]. Defences for this threat aim to detect and ﬁlter malicious training data [5”
Adversarial Robustness Toolbox v1.0.0 (2018, background): “This lack of control creates a threat of poisoning attacks where adversaries have the opportunity of manipulating the training data to signiﬁcantly decrease overall performance, cause targeted misclassiﬁcation or bad behavior, and insert backdoors and neural trojans [5, 43, 28, 25, 33, 34, 7, 42].”