  We introduce a novel clean-label targeted poisoning attack on learning
mechanisms. While classical poisoning attacks typically corrupt data via
addition, modification and omission, our attack focuses on data omission only.
Our attack misclassifies a single, targeted test sample of choice, without
manipulating that sample. We demonstrate the effectiveness of omission attacks
against a large variety of learners including deep neural networks, SVM and
decision trees, using several datasets including MNIST, IMDB and CIFAR. The
focus of our attack on data omission only is beneficial as well, as it is
simpler to implement and analyze. We show that, with a low attack budget, our
attack's success rate is above 80%, and in some cases 100%, for white-box
learning. It is systematically above the reference benchmark for black-box
learning. For both white-box and black-box cases, changes in model accuracy are
negligible, regardless of the specific learner and dataset. We also prove
theoretically in a simplified agnostic PAC learning framework that, subject to
dataset size and distribution, our omission attack succeeds with high
probability against any successful simplified agnostic PAC learner.
