Spread the few poisoned training examples across feature-space clusters: DFCS picks one centroid-nearest sample per cluster and beats six prior selectors in all six low-poisoning settings.
Proceedings of the IEEE/CVF International Conference on Computer Vision , pages =
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Diversity Matters: Distributional Feature Coverage Sample Selection for Data-Efficient Backdoor Attacks
Spread the few poisoned training examples across feature-space clusters: DFCS picks one centroid-nearest sample per cluster and beats six prior selectors in all six low-poisoning settings.