Yi Yang

Diversity Matters: Distributional Feature Coverage Sample Selection for Data-Efficient Backdoor Attacks

DFCS is proposed, a training-free, trigger-agnostic method that clusters fixed pretrained features into one region per poisoning slot and selects the centroid-nearest sample from each region and supports distributional feature coverage as an effective selection principle for low-budget dirty-label backdoor attacks.