Poisoning efficiency is a crucial factor in poisoning-based backdoor attacks. Attackers prefer to use as few poisoned samples as possible to achieve the same level of attack strength, in order to remain undetected. Efficient triggers have significantly improved poisoning efficiency, but there is still room for improvement. Recently, selecting efficient samples has shown promise, but it requires a proxy backdoor injection task to find an efficient poisoned sample set, which can lead to performance degradation if the proxy attack settings are different from the actual settings used by the victims. In this paper, we propose a novel Proxy-Free Strategy (PFS) that selects efficient poisoned samples based on individual similarity and set diversity, effectively addressing this issue. We evaluate the proposed strategy on several datasets, triggers, poisoning ratios, architectures, and training hyperparameters. Our experimental results demonstrate that PFS achieves higher backdoor attack strength while x500 faster than previous proxy-based selection approaches.

本文提出了一种基于个体相似性和集合多样性选择高效毒化样本的代理自由策略（PFS），可以有效地解决代理攻击设置与受害者实际设置不同的问题，实验结果表明PFS比前面基于代理的选择方法速度快500倍并取得更高的背门攻击强度。

无中间人的策略在后门攻击中实现提高毒化效率