BackdoorBench: A Comprehensive Benchmark and Analysis of Backdoor Learning

Understand

As an emerging and vital topic for studying deep neural networks' vulnerability (DNNs), backdoor learning has attracted increasing interest in recent years, and many seminal backdoor attack and defense algorithms are being developed successively or concurrently, in the status of a rapid arms race.

• However, mainly due to the diverse settings, and the difficulties of implementation and reproducibility of existing works, there is a lack of a unified and standardized benchmark of backdoor learning, causing unfair comparisons, and unreliable conclusions (e.g., misleading, biased or even false conclusions).
• Consequently, it is difficult to evaluate the current progress and design the future development roadmap of this literature.
• To alleviate this dilemma, we build a comprehensive benchmark of backdoor learning called BackdoorBench.