Machine learning is vulnerable to a wide variety of attacks. It is now well
understood that by changing the underlying data distribution, an adversary can
poison the model trained with it or introduce backdoors. In this paper we
present a novel class of training-time attacks that require no changes to the
underlying dataset or model architecture, but instead only change the order in
which data are supplied to the model. In particular, we find that the attacker
can either prevent the model from learning, or poison it to learn behaviours
specified by the attacker. Furthermore, we find that even a single
adversarially-ordered epoch can be enough to slow down model learning, or even
to reset all of the learning progress. Indeed, the attacks presented here are
not specific to the model or dataset, but rather target the stochastic nature
of modern learning procedures. We extensively evaluate our attacks on computer
vision and natural language benchmarks to find that the adversary can disrupt
model training and even introduce backdoors.

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