Backdoor learning is an emerging topic of studying the adversarial
vulnerability of
machine learning models during the training stage. Many backdoor attack
and defense
methods have been developed in recent ML and Security
conferences/journals. It is
important to build a benchmark to review the current progress and
facilitate future
research in backdoor learning.
BackdoorBench aims to provide an easy implementation of both
backdoor attack and
backdoor defense methods to facilitate future research, as well as a comprehensive evaluation of
existing attack and
defense methods.
This benchmark will be continuously updated to track the lastest
advances of backddor
learning, including the
implementations of more backddor methods, as well as their evaluations
in the
leaderboard. You are welcome to contribute
your backdoor methods to BackdoorBench.
BackdoorBench defines a realistic threat model where attackers and defenders can compete with each other under unified settings, which facilitates fair comparisons of various methods.
BackdoorBench provides a coding framework with a modular design, which facilitates the implementation of all attacks, defenses, and related evaluation processes.
BackdoorBench guarantees high reproducibility of all results on the leaderboards, by providing all necessary terms including implementation of methods, hyper-parameters, trained models, easy-to-use scripts, etc.
Here are the related papers.