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Data poisoning attacks -- where an adversary can modify a small fraction oftraining data, with the goal of forcing the trained classifier to high loss --are an important threat for machine learning in many applications. While a bodyof prior work has developed attacks and defenses, there is not much generalunderstanding on when various attacks and defenses are effective. In this work,we undertake a rigorous study of defenses against data poisoning for onlinelearning. First, we study four standard defenses in a powerful threat model,and provide conditions under which they can allow or resist rapid poisoning. Wethen consider a weaker and more realistic threat model, and show that thesuccess of the adversary in the presence of data poisoning defenses theredepends on the "ease" of the learning problem.

An Investigation of Data Poisoning Defenses for Online Learning