A Proxy-Free Strategy for Practically Improving the Poisoning Efficiency
  in Backdoor Attacks
Ziqiang Li Hong Sun Pengfei Xia Beihao Xia Xue Rui Wei Zhang Bin Li
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.