Description
Adversarial Robustness Toolbox (ART) is a comprehensive Python library that focuses on the security of machine learning models. As a project under the LF AI & Data Foundation, ART provides developers and researchers with the necessary tools to evaluate, defend, and verify machine learning models and applications against adversarial threats. Originally developed by IBM, ART was moved to the LF AI in July 2020, highlighting its significance in the AI community.
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The toolbox supports a wide range of popular machine learning frameworks, including TensorFlow, Keras, PyTorch, MXNet, scikit-learn, XGBoost, LightGBM, CatBoost, and GPy. It is versatile in handling various data types such as images, tables, audio, and video, and is applicable to numerous machine learning tasks like classification, object detection, generation, and certification.
ART is equipped with 39 attack modules categorized into four main types: Evasion, Poisoning, Extraction, and Inference. These modules allow users to simulate and study different adversarial scenarios. In addition, ART offers 29 defense modules, which are organized into five categories: Preprocessor, Postprocessor, Trainer, Transformer, and Detector. These defense mechanisms help in fortifying models against potential adversarial attacks.
The toolbox also includes support for three robustness metrics, one certification metric, and one verification metric, providing a comprehensive suite for assessing the robustness of machine learning models. Users can find detailed information about these metrics and estimators in the user guide, along with example notebooks to facilitate getting started with ART.
ART encourages community involvement through its GitHub repository, where users can contribute to its development. The project also maintains three mailing lists to cater to different interests, ranging from milestone announcements to technical discussions. This open-source project is a valuable resource for anyone looking to enhance the security and robustness of their AI models.
- CategoryAI Cybersecurity Tools
- Pricing ModelOpen source
- DeploymentCloud
- PlatformsTensorFlow, Keras, PyTorch, MXNet, scikit-learn, XGBoost, LightGBM, CatBoost, GPy
Adversarial Robustness Toolbox's Core Features
Supports popular ML frameworks
Handles diverse data types
39 attack modules
29 defense modules
Robustness metrics included
Certification and verification metrics
Community-driven development
Open-source project
Getting Started with Adversarial Robustness Toolbox
Configure: Set up the toolbox using the user guide
Use: Implement attack and defense modules
Evaluate: Assess model robustness with metrics
Contribute: Join the community on GitHub
Adversarial Robustness Toolbox's Use Cases
- Model Evaluation
- Adversarial Defense
- Security Research
- Community Contribution
- Educational Purposes
