adversarial-attacks

Data augmentation for NLP

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

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

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

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.

Must-read Papers on Textual Adversarial Attack and Defense

A Toolbox for Adversarial Robustness Research

PyTorch implementation of adversarial attacks.

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

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

An Open-Source Package for Textual Adversarial Attack.

A Harder ImageNet Test Set (CVPR 2021)

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

A Model for Natural Language Attack on Text Classification and Inference

Implementation of Papers on Adversarial Examples

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

Adversarial attacks and defenses on Graph Neural Networks.

Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models (published in ICLR2018)

🔥🔥Defending Against Deepfakes Using Adversarial Attacks on Conditional Image Translation Networks

Simple pytorch implementation of FGSM and I-FGSM

Implementation of the paper "Adversarial Attacks on Neural Networks for Graph Data".

Code for our nips19 paper: You Only Propagate Once: Accelerating Adversarial Training Via Maximal Principle

DEEPSEC: A Uniform Platform for Security Analysis of Deep Learning Model

Awesome Resources for Advanced Computer Vision Topics

TrojanZoo provides a universal pytorch platform to conduct security researches (especially backdoor attacks/defenses) of image classification in deep learning.

Implementation of the KDD 2020 paper "Graph Structure Learning for Robust Graph Neural Networks"

Official TensorFlow Implementation of Adversarial Training for Free! which trains robust models at no extra cost compared to natural training.

A curated list of papers on adversarial machine learning (adversarial examples and defense methods).