adversarial-machine-learning

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

A Toolbox for Adversarial Robustness Research

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

T2F: text to face generation using Deep Learning

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 (GNNs).

RobustBench: a standardized adversarial robustness benchmark [NeurIPS'21 Benchmarks and Datasets Track]

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

Provable adversarial robustness at ImageNet scale

Code for our NeurIPS 2019 *spotlight* "Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers"

Backdoors Framework for Deep Learning and Federated Learning. A light-weight tool to conduct your research on backdoors.

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

[NeurIPS 2020]auto_LiRPA: An Automatic Linear Relaxation based Perturbation Analysis Library for Neural Networks

💡 Adversarial attacks on model explanations, and evaluation approaches

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

MSG-GAN: Multi-Scale Gradients GAN (Architecture inspired from ProGAN but doesn't use layer-wise growing)

Physical adversarial attack for fooling the Faster R-CNN object detector

Create adversarial attacks against machine learning Windows malware detectors

Adversarial Training for Neural Relation Extraction

A guided mutation-based fuzzer for ML-based Web Application Firewalls

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 library for running membership inference attacks against ML models

Plausible looking adversarial examples for text classification

A PyTorch Toolbox for creating adversarial examples that fool neural networks.

SHIELD: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression

A simple GUI tool for generating adversarial poses of objects.