There are 53 repositories under adversarial-machine-learning topic.
Fawkes, privacy preserving tool against facial recognition systems. More info at https://sandlab.cs.uchicago.edu/fawkes
Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams
TextAttack 🐙 is a Python framework for adversarial attacks, data augmentation, and model training in NLP https://textattack.readthedocs.io/en/master/
A Toolbox for Adversarial Robustness Research
A curated list of useful resources that cover Offensive AI.
A curated list of adversarial attacks and defenses papers on graph-structured data.
RobustBench: a standardized adversarial robustness benchmark [NeurIPS'21 Benchmarks and Datasets Track]
Unofficial PyTorch implementation of the paper titled "Progressive growing of GANs for improved Quality, Stability, and Variation"
GraphGallery is a gallery for benchmarking Graph Neural Networks, From InplusLab.
Security and Privacy Risk Simulator for Machine Learning (arXiv:2312.17667)
Backdoors Framework for Deep Learning and Federated Learning. A light-weight tool to conduct your research on backdoors.
A curated list of trustworthy deep learning papers. Daily updating...
💡 Adversarial attacks on explanations and how to defend them
auto_LiRPA: An Automatic Linear Relaxation based Perturbation Analysis Library for Neural Networks and General Computational Graphs
Code for our NeurIPS 2019 *spotlight* "Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers"
A curated list of papers on adversarial machine learning (adversarial examples and defense methods).
Create adversarial attacks against machine learning Windows malware detectors
Official TensorFlow Implementation of Adversarial Training for Free! which trains robust models at no extra cost compared to natural training.
This repository explores the variety of techniques and algorithms commonly used in deep learning and the implementation in MATLAB and PYTHON
The goal of this survey is two-fold: (i) to present recent advances on adversarial machine learning (AML) for the security of RS (i.e., attacking and defense recommendation models), (ii) to show another successful application of AML in generative adversarial networks (GANs) for generative applications, thanks to their ability for learning (high-dimensional) data distributions. In this survey, we provide an exhaustive literature review of 74 articles published in major RS and ML journals and conferences. This review serves as a reference for the RS community, working on the security of RS or on generative models using GANs to improve their quality.
A guided mutation-based fuzzer for ML-based Web Application Firewalls
Physical adversarial attack for fooling the Faster R-CNN object detector
MSG-GAN: Multi-Scale Gradients GAN (Architecture inspired from ProGAN but doesn't use layer-wise growing)
TransferAttack is a pytorch framework to boost the adversarial transferability for image classification.
A library for running membership inference attacks against ML models
A curated collection of adversarial attack and defense on recommender systems.
A curated list of academic events on AI Security & Privacy