  Semi-Supervised Learning (SSL) aims to maximize the benefits of learning from
a limited amount of labelled data together with a vast amount of unlabelled
data. Because they rely on the known labels to infer the unknown labels, SSL
algorithms are sensitive to data quality. This makes it important to study the
potential threats related to the labelled data, more specifically, label
poisoning. However, data poisoning of SSL remains largely understudied. To fill
this gap, we propose a novel data poisoning method which is both effective and
efficient. Our method exploits mathematical properties of SSL to approximate
the influence of labelled inputs onto unlabelled one, which allows the
identification of the inputs that, if poisoned, would produce the highest
number of incorrectly inferred labels. We evaluate our approach on three
classification problems under 12 different experimental settings each. Compared
to the state of the art, our influence-based attack produces an average
increase of error rate 3 times higher, while being faster by multiple orders of
magnitude. Moreover, our method can inform engineers of inputs that deserve
investigation (relabelling them) before training the learning model. We show
that relabelling one-third of the poisoned inputs (selected based on their
influence) reduces the poisoning effect by 50%.
