Scholarlink

<p>In recent years, backdoor learning has attracted increasing attention due to its effectiveness on investigating the adversarial vulnerability of artificial intelligence (AI) systems. Several seminal backdoor attack and defense algorithms have been developed, forming an increasingly fierce arms race. However, since backdoor learning involves various factors in different stages of an AI system (e.g., data preprocessing, model training algorithm, model activation), there have been diverse settings in existing works, causing unfair comparisons or unreliable conclusions (e.g., misleading, biased, or even false conclusions). Hence, it is urgent to build a unified and standardized benchmark of backdoor learning, such that we can track real progress and design a roadmap for the future development of this literature. To that end, we construct a comprehensive benchmark of backdoor learning, dubbed <i>BackdoorBench</i>. Our benchmark makes three valuable contributions to the research community. (1) We provide an integrated implementation of representative backdoor learning algorithms (currently including 20 attack algorithms and 32 defense algorithms), based on an extensible modular-based codebase. (2) We conduct comprehensive evaluations of the implemented algorithms on 4 models and 4 datasets, leading to 11,492 pairs of attack-against-defense evaluations in total. (3) Based on above evaluations, we present abundant analysis from 10 perspectives via 23 analysis tools, and reveal several inspiring insights about backdoor learning. We hope that our efforts could build a solid foundation of backdoor learning to facilitate researchers to investigate existing algorithms, develop more innovative algorithms, and explore the intrinsic mechanism of backdoor learning. Finally, we have created a user-friendly website at <a href="https://backdoorbench.github.io/">https://backdoorbench.github.io/</a>, which collects all the important information of BackdoorBench, including the link to Codebase, Docs, Leaderboard, and Model Zoo.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

BackdoorBench: A Comprehensive Benchmark and Analysis of Backdoor Learning

  • 期刊论文

  • 发表日期：

    2025年5月6日

查看全文
  • Baoyuan Wu,
  • Hongrui Chen,
  • Mingda Zhang,
  • Zihao Zhu,
  • Shaokui Wei,
  • Danni Yuan,
  • Mingli Zhu,
  • Ruotong Wang,
  • Li Liu,
  • Chao Shen

摘要

In recent years, backdoor learning has attracted increasing attention due to its effectiveness on investigating the adversarial vulnerability of artificial intelligence (AI) systems. Several seminal backdoor attack and defense algorithms have been developed, forming an increasingly fierce arms race. However, since backdoor learning involves various factors in different stages of an AI system (e.g., data preprocessing, model training algorithm, model activation), there have been diverse settings in existing works, causing unfair comparisons or unreliable conclusions (e.g., misleading, biased, or even false conclusions). Hence, it is urgent to build a unified and standardized benchmark of backdoor learning, such that we can track real progress and design a roadmap for the future development of this literature. To that end, we construct a comprehensive benchmark of backdoor learning, dubbed BackdoorBench. Our benchmark makes three valuable contributions to the research community. (1) We provide an integrated implementation of representative backdoor learning algorithms (currently including 20 attack algorithms and 32 defense algorithms), based on an extensible modular-based codebase. (2) We conduct comprehensive evaluations of the implemented algorithms on 4 models and 4 datasets, leading to 11,492 pairs of attack-against-defense evaluations in total. (3) Based on above evaluations, we present abundant analysis from 10 perspectives via 23 analysis tools, and reveal several inspiring insights about backdoor learning. We hope that our efforts could build a solid foundation of backdoor learning to facilitate researchers to investigate existing algorithms, develop more innovative algorithms, and explore the intrinsic mechanism of backdoor learning. Finally, we have created a user-friendly website at https://backdoorbench.github.io/, which collects all the important information of BackdoorBench, including the link to Codebase, Docs, Leaderboard, and Model Zoo.