16d11e9595188dbad0418a85f0351aba-Supplemental.pdf

This section introduces more backgrounds on poisoning attacks and backdoor attacks, and details on the adversarial attacks that we use to craft accumulative poisoning samples in our methods. Finally, we describe the commonly used anomaly detection methods against adversarially crafted samples, following previous settings [40]. B.1 Poisoning attacks and backdoor attacks There is extensive prior work on poisoning attacks, especially in the offline settings against SVM [3], logistic regression [36], collaborative filtering [27], feature selection [54], clustering [8], and neural networks [9, 21, 22, 38, 50]. Poisoning attacks in real-time data streaming are studied on online SVM [4], autoregressive models [1, 7], bandit algorithms [20, 31, 33], and classification [26, 52, 57]. Compared to poisoning attacks, backdoor attacks draw attention in more recent researches.