Universal Detection of Backdoor Attacks via Density-based Clustering and Centroids Analysis
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In this paper, we propose a Universal Defence based on Clustering and Centroids Analysis (CCA-UD) against backdoor attacks. The goal of the proposed defence is to reveal whether a Deep Neural Network model is subject to a backdoor attack by inspecting the training dataset. CCA-UD first clusters the samples of the training set by means of density-based clustering. Then, it applies a novel strategy to detect the presence of poisoned clusters. The proposed strategy is based on a general misclassification behaviour obtained when the features of a representative example of the analysed cluster are added to benign samples. The capability of inducing a misclassification error is a general characteristic of poisoned samples, hence the proposed defence is attack-agnostic. This mask a significant difference with respect to existing defences, that, either can defend against only some types of backdoor attacks, e.g., when the attacker corrupts the label of the poisoned samples, or are effective only when some conditions on the poisoning ratios adopted by the attacker or the kind of triggering pattern used by the attacker are satisfied. Experiments carried out on several classification tasks, considering different types of backdoor attacks and triggering patterns, including both local and global triggers, reveal that the proposed method is very effective to defend against backdoor attacks in all the cases, always outperforming the state of the art techniques.
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