Popular GitHub Repositories for Certified Robustness

Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams

An open-source data logging library for machine learning models and data pipelines. ? Provides visibility into data quality & model performance over time. ?️ Supports privacy-preserving data collection, ensuring safety & robustness. ?

A unified evaluation framework for large language models

Advbox is a toolbox to generate adversarial examples that fool neural networks in PaddlePaddle、PyTorch、Caffe2、MxNet、Keras、TensorFlow and Advbox can benchmark the robustness of machine learning models. Advbox give a command line tool to generate adversarial examples with Zero-Coding.

A Toolbox for Adversarial Robustness Research

关于domain generalization，domain adaptation，causality，robutness，prompt，optimization，generative model各式各样研究的阅读笔记

Corruption and Perturbation Robustness (ICLR 2019)

A unified ensemble framework for PyTorch to improve the performance and robustness of your deep learning model.

Benchmarking Generalized Out-of-Distribution Detection

Out-of-distribution detection, robustness, and generalization resources. The repository contains a curated list of papers, tutorials, books, videos, articles and open-source libraries etc

Awesome-LLM-Robustness: a curated list of Uncertainty, Reliability and Robustness in Large Language Models

PyBullet CartPole and Quadrotor environments—with CasADi symbolic a priori dynamics—for learning-based control and RL

RobustBench: a standardized adversarial robustness benchmark [NeurIPS 2021 Benchmarks and Datasets Track]

Code relative to "Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks"

Raising the Cost of Malicious AI-Powered Image Editing

Code and information for face image quality assessment with SER-FIQ

A Python library for adversarial machine learning focusing on benchmarking adversarial robustness.

Diffusion Classifier leverages pretrained diffusion models to perform zero-shot classification without additional training

Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities. arXiv:2408.07666.

Provable adversarial robustness at ImageNet scale