adversarial-attacks

Adversary Emulation Framework

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

Data augmentation for NLP

TextAttack 🐙 is a Python framework for adversarial attacks, data augmentation, and model training in NLP https://textattack.readthedocs.io/en/master/

A Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, and JAX

A unified evaluation framework for large language models

PyTorch implementation of adversarial attacks [torchattacks]

Must-read Papers on Textual Adversarial Attack and Defense

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 reading list for large models safety, security, and privacy (including Awesome LLM Security, Safety, etc.).

A Toolbox for Adversarial Robustness Research

A pytorch adversarial library for attack and defense methods on images and graphs

A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.

A curated list of adversarial attacks and defenses papers on graph-structured data.

An Open-Source Package for Textual Adversarial Attack.

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

A Harder ImageNet Test Set (CVPR 2021)

Raising the Cost of Malicious AI-Powered Image Editing

A Model for Natural Language Attack on Text Classification and Inference

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