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Adversarial Robustness Toolbox
Adversarial Robustness Toolbox is a Python library for machine learning security that defends and evaluates models against evasion, poisoning, extraction, and inference threats.
Adversarial Robustness Toolbox is a Python library hosted by the Linux Foundation AI & Data Foundation designed for machine learning security. It provides tools for developers and researchers to evaluate and defend models against adversarial threats including evasion, poisoning, extraction, and inference. The library supports major machine learning frameworks like TensorFlow, Keras, PyTorch, scikit-learn, XGBoost, LightGBM, CatBoost, and GPy. It accommodates all common data types such as images, tables, audio, and video. It handles various machine learning tasks including classification, object detection, speech recognition, generation, and certification. The project receives support from DARPA and operates under open source development.
Adversarial Robustness Toolbox at a glance
Key features
- Support for major ML frameworks like TensorFlow, PyTorch, and scikit-learn
- Defense against adversarial threats like evasion and poisoning
- Evaluation tools for model extraction and inference attacks
- Compatibility with images, tables, audio, and video data types
- Task support for classification, object detection, and speech recognition
- Certification and generation task capabilities
Use it for
- Defending machine learning models against adversarial threats
- Evaluating model security against evasion and poisoning
- Testing machine learning applications for extraction and inference vulnerabilities
- Securing models across classification and object detection tasks
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