In the rapidly evolving landscape of Artificial Intelligence, the security and trustworthiness of deep learning models are paramount. One significant threat is the “backdoor attack,” where malicious vulnerabilities are subtly injected into models during their training phase. These hidden backdoors can later be exploited to manipulate the model’s behavior under specific, often imperceptible, conditions.
This is where BackdoorBench comes into play. Developed by the SCLBD team at The Chinese University of Hong Kong, Shenzhen, BackdoorBench stands as a comprehensive benchmark designed to evaluate and compare various backdoor attack and defense methods. It provides researchers and practitioners with easy-to-use implementations of mainstream techniques, fostering a deeper understanding of these critical security challenges.
BackdoorBench addresses a crucial need in AI security by offering:
Standardized Evaluation: It provides a consistent platform to test and compare the effectiveness of different attacks and defenses.
Easy Implementation: Simplifies the process of experimenting with complex backdoor techniques.
Continuous Updates: The project is actively maintained, incorporating the latest advancements in backdoor learning research.
Public Leaderboard: Offers a transparent way to track the performance of various methods.
BackdoorBench is equipped with a rich set of features to facilitate comprehensive research:
Extensive Attack Methods: Includes 16 diverse backdoor attack techniques, ranging from classic BadNets to advanced Input-aware and WaNet attacks.
Robust Defense Strategies: Features 28 different defense and detection methods, such as ABL, Neural Cleanse, STRIP, and many more, aimed at mitigating or identifying backdoors.
Diverse Datasets: Supports popular datasets like CIFAR-10, CIFAR-100, GTSRB, and Tiny ImageNet for varied testing scenarios.
Multiple Model Architectures: Compatible with a range of deep learning models, including PreAct-Resnet18, VGG19_bn, ConvNeXT_tiny, and Vision Transformers.
Powerful Analysis Tools: Offers a suite of tools for in-depth model and data analysis, including T-SNE, Grad-CAM, Neuron Activation, and Loss Landscape visualization.
To gain a more visual and interactive understanding of BackdoorBench’s capabilities, its ecosystem, and how various attacks and defenses operate, I’ve created a dedicated interactive infographic.
Dive into the details and interact with the data here:
BackdoorBench Interactive Infographic
This infographic provides a dynamic overview of the project’s scope, key metrics, and a simplified workflow, making complex concepts more accessible.
BackdoorBench is an open-source initiative, and contributions from the community are highly welcomed. Whether you’re looking to implement new methods, improve existing ones, or simply explore the world of AI security, BackdoorBench offers a valuable resource.
For more information, visit the official BackdoorBench GitHub Repository.