Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams
-
Updated
Jun 13, 2024 - Python
Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams
🐢 Open-Source Evaluation & Testing for LLMs and ML models
[ACL 2024] An Easy-to-use Knowledge Editing Framework for LLMs.
The open-sourced Python toolbox for backdoor attacks and defenses.
[ICML 2024] TrustLLM: Trustworthiness in Large Language Models
[NeurIPS-2023] Annual Conference on Neural Information Processing Systems
🚀 A fast safe reinforcement learning library in PyTorch
Code of the paper: A Recipe for Watermarking Diffusion Models
Neural Network Verification Software Tool
A comprehensive toolbox for model inversion attacks and defenses, which is easy to get started.
Official code repo for the O'Reilly Book - Machine Learning for High-Risk Applications
A toolkit for tools and techniques related to the privacy and compliance of AI models.
Moonshot - A simple and modular tool to evaluate and red-team any LLM application.
A project to add scalable state-of-the-art out-of-distribution detection (open set recognition) support by changing two lines of code! Perform efficient inferences (i.e., do not increase inference time) and detection without classification accuracy drop, hyperparameter tuning, or collecting additional data.
The official implementation for ICLR23 paper "GNNSafe: Energy-based Out-of-Distribution Detection for Graph Neural Networks"
[ICCV2021 Oral] Fooling LiDAR by Attacking GPS Trajectory
[ACM MM22] Towards Robust Video Object Segmentation with Adaptive Object Calibration, ACM Multimedia 2022
Principal Image Sections Mapping. Convolutional Neural Network Visualisation and Explanation Framework
A project to improve out-of-distribution detection (open set recognition) and uncertainty estimation by changing a few lines of code in your project! Perform efficient inferences (i.e., do not increase inference time) without repetitive model training, hyperparameter tuning, or collecting additional data.
Add a description, image, and links to the trustworthy-ai topic page so that developers can more easily learn about it.
To associate your repository with the trustworthy-ai topic, visit your repo's landing page and select "manage topics."