A Comprehensive Benchmark for Evaluating Backdoor Attacks and Defenses

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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.

Unified Evaluation

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.

Modular Framework

BackdoorBench provides a coding framework with a modular design, which facilitates the implementation of all attacks, defenses, and related evaluation processes.

High Reproducibility

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.

Our papers

Here are the related papers.

BackdoorBench: A Comprehensive Benchmark of Backdoor Learning

Effective Backdoor Defense by Exploiting Sensitivity of Poisoned Samples

Backdoor Defense via Decoupling the Training Process

Invisible Backdoor Attack With Sample-Specific Triggers