Density-aware Sample-specific Attack
Despite recent progress in backdoor attacks, existing methods remain susceptible to post-training defenses that erase the backdoor through fine-tuning or pruning. We revisit the core objectives of backdoor attacks and derive principled criteria characterizing optimal sample-specific trigger construction under a Bayes-optimal model of the victim's training. Our analysis reveals that both attack success and clean-accuracy preservation are simultaneously optimized when triggered samples are steered into low-density regions of the clean data distribution, a distributional condition that controls all moments of the poisoned distribution at once rather than a handful of input-space summary stat...
| Authors | Qiyuan Wang, Yao Li, Raymond K. W. Wong |
|---|---|
| Primary category | cs.LG |
| Published | 27 May 2026 |
| Last updated at source | 28 May 2026 |
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