Dataset distillation offers a potential means to enhance data efficiency in
deep learning. Recent studies have shown its ability to counteract backdoor
risks present in original training samples. In this study, we delve into the
theoretical aspects of backdoor attacks and dataset distillation based on
kernel methods. We introduce two new theory-driven trigger pattern generation
methods specialized for dataset distillation. Following a comprehensive set of
analyses and experiments, we show that our optimization-based trigger design
framework informs effective backdoor attacks on dataset distillation. Notably,
datasets poisoned by our designed trigger prove resilient against conventional
backdoor attack detection and mitigation methods. Our empirical results
validate that the triggers developed using our approaches are proficient at
executing resilient backdoor attacks.

基于核方法的数据集精简是数据效率提升的一种潜在手段，本研究通过理论研究与实验分析，提出了专门用于数据集精简的两种新的基于理论的触发器模式生成方法，证明了我们的基于优化的触发器设计框架可以有效地实施反向攻击，并验证了使用我们方法开发的触发器在执行弹性反向攻击方面的熟练程度。