  Backdoor learning is an emerging and vital topic for studying deep neural
networks' vulnerability (DNNs). Many pioneering backdoor attack and defense
methods are being proposed, successively or concurrently, in the status of a
rapid arms race. However, we find that the evaluations of new methods are often
unthorough to verify their claims and accurate performance, mainly due to the
rapid development, diverse settings, and the difficulties of implementation and
reproducibility. Without thorough evaluations and comparisons, it is not easy
to track the current progress and design the future development roadmap of the
literature. To alleviate this dilemma, we build a comprehensive benchmark of
backdoor learning called BackdoorBench. It consists of an extensible
modular-based codebase (currently including implementations of 8
state-of-the-art (SOTA) attacks and 9 SOTA defense algorithms) and a
standardized protocol of complete backdoor learning. We also provide
comprehensive evaluations of every pair of 8 attacks against 9 defenses, with 5
poisoning ratios, based on 5 models and 4 datasets, thus 8,000 pairs of
evaluations in total. We present abundant analysis from different perspectives
about these 8,000 evaluations, studying the effects of different factors in
backdoor learning. All codes and evaluations of BackdoorBench are publicly
available at \url{https://backdoorbench.github.io}.
