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IBM Adversarial Robustness Toolbox (ART)

Rating:

4.7 / 5.0

IBM Adversarial Robustness Toolbox (ART)

Tags

AI, machine learning security, adversarial attacks, model robustness, open-source, TensorFlow, PyTorch, scikit-learn

Categories:
Ethical AI and Safety
Creator IBM
Date 2018-07-03
Platforms Python
Status Active
Website github.com
Price Model Free (Open Source)
Sections:
AI Model SecurityAI Risk Management

Pricing Details

Free and open-source under the MIT license.

Features

Tools for adversarial machine learning, Defense against adversarial attacks, Model evaluation for robustness, Integration with TensorFlow, Keras, PyTorch, and Scikit-learn, Robustness metrics and analysis, Detection and mitigation of adversarial inputs, Enhances model security and reliability.

Integrations

Integrates with TensorFlow, PyTorch, Keras, MXNet, scikit-learn, XGBoost, LightGBM, and CatBoost.

Preview

IBM's Adversarial Robustness Toolbox (ART) offers a comprehensive suite of tools for enhancing the security of machine learning models. It provides over 39 attack modules and 29 defense mechanisms, enabling users to simulate adversarial scenarios and implement robust countermeasures. ART supports various machine learning tasks, including classification, object detection, speech recognition, and generative models. Its integration capabilities span popular frameworks like TensorFlow, PyTorch, and scikit-learn, making it a versatile choice for both academic research and enterprise applications.