SNEAKDOOR: Stealthy Backdoor Attacks against Distribution Matching-based Dataset Condensation
Dataset condensation aims to synthesize compact yet informative datasets that retain the training efficacy of full-scale data, offering substantial gains in efficiency. Recent studies reveal that the condensation process can be vulnerable to backdoor attacks, where malicious triggers are injected into the condensation dataset, manipulating model behavior during inference. While prior approaches have made progress in balancing attack success rate and clean test accuracy, they often fall short in preserving stealthiness, especially in concealing the visual artifacts of condensed data or the perturbations introduced during inference. To address this challenge, we introduce Sneakdoor, which e...
| Authors | He Yang, Dongyi Lv, Song Ma et al. |
|---|---|
| Primary category | cs.CR |
| Published | 29 March 2026 |
| Last updated at source | 29 March 2026 |
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