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

T2F: text to face generation using Deep Learning

ProGAN package implemented as an extension of PyTorch nn.Module

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

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

GraphGallery is a gallery for benchmarking Graph Neural Networks (GNNs) and Graph Adversarial Learning with TensorFlow 2.x and PyTorch backend.

Provable adversarial robustness at ImageNet scale

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

RobustBench: a standardized adversarial robustness benchmark [arXiv, Oct 2020]

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

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

Adversarial Training for Neural Relation Extraction

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

Plausible looking adversarial examples for text classification

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

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

💡 Adversarial attacks on model explanations and evaluation approaches

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

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

A library for running membership inference attacks against ML models

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.

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

A simple GUI tool for generating adversarial poses of objects.

Code repository for the paper "Adversarial Deep Learning for Robust Detection of Binary Encoded Malware"

Certified defense to adversarial examples using CROWN and IBP. Also includes GPU implementation of CROWN verification algorithm (in PyTorch).