  Data poisoning attacks aim at manipulating model behaviors through distorting
training data. Previously, an aggregation-based certified defense, Deep
Partition Aggregation (DPA), was proposed to mitigate this threat. DPA predicts
through an aggregation of base classifiers trained on disjoint subsets of data,
thus restricting its sensitivity to dataset distortions. In this work, we
propose an improved certified defense against general poisoning attacks, namely
Finite Aggregation. In contrast to DPA, which directly splits the training set
into disjoint subsets, our method first splits the training set into smaller
disjoint subsets and then combines duplicates of them to build larger (but not
disjoint) subsets for training base classifiers. This reduces the worst-case
impacts of poison samples and thus improves certified robustness bounds. In
addition, we offer an alternative view of our method, bridging the designs of
deterministic and stochastic aggregation-based certified defenses. Empirically,
our proposed Finite Aggregation consistently improves certificates on MNIST,
CIFAR-10, and GTSRB, boosting certified fractions by up to 3.05%, 3.87% and
4.77%, respectively, while keeping the same clean accuracies as DPA's,
effectively establishing a new state of the art in (pointwise) certified
robustness against data poisoning.
