Improved Certified Defenses against Data Poisoning with (Deterministic) Finite Aggregation
This work addresses security vulnerabilities in ML systems for practitioners by providing incremental improvements in certified defenses against data poisoning attacks.
The paper tackles the problem of data poisoning attacks on machine learning models by proposing Finite Aggregation, an improved certified defense that enhances robustness bounds over prior methods, achieving certified fraction boosts of up to 3.05% on MNIST, 3.87% on CIFAR-10, and 4.77% on GTSRB while maintaining clean accuracy.
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.