SNEAKDOOR: Stealthy Backdoor Attacks against Distribution Matching-based Dataset Condensation

Dataset condensation aims to synthesize compact yet informative datasets that1 retain the training efficacy of full-scale data, offering substantial gains in efficiency.2 Recent studies reveal that the condensation process can be vulnerable to backdoor3 attacks, where malicious triggers are injected into the condensation dataset, manipu-4 lating model behavior during inference. While prior approaches have made progress5 in balancing attack success rate and clean test accuracy, they often fall short in6 preserving stealthiness, especially in concealing the visual artifacts of condensed7 data or the perturbations introduced during inference. To address this challenge,8 we introduce SNEAKDOOR, which enhances stealthiness without compromising9 attack effectiveness. SNEAKDOOR exploits the inherent vulnerability of class deci-10 sion boundaries and incorporates a generative module that constructs input-aware11 triggers aligned with local feature geometry, thereby minimizing detectability. This12 joint design enables the attack to remain imperceptible to both human inspection13 and statistical detection. Extensive experiments across multiple datasets demon-14 strate that SNEAKDOOR achieves a compelling balance among attack success rate,15 clean test accuracy, and stealthiness, substantially improving the invisibility of both16 the synthetic data and triggered samples while maintaining high attack efficacy.17