adversarial-defense

Must-read Papers on Textual Adversarial Attack and Defense

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

auto_LiRPA: An Automatic Linear Relaxation based Perturbation Analysis Library for Neural Networks and General Computational Graphs

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

A list of awesome resources for adversarial attack and defense method in deep learning

CVPR 2022 Workshop Robust Classification

Provably defending pretrained classifiers including the Azure, Google, AWS, and Clarifai APIs

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

This repository contains the implementation of three adversarial example attack methods FGSM, IFGSM, MI-FGSM and one Distillation as defense against all attacks using MNIST dataset.

[ICLR 2021] "InfoBERT: Improving Robustness of Language Models from An Information Theoretic Perspective" by Boxin Wang, Shuohang Wang, Yu Cheng, Zhe Gan, Ruoxi Jia, Bo Li, Jingjing Liu

Adversarial attacks on Deep Reinforcement Learning (RL)

Adversarial Distributional Training (NeurIPS 2020)

😎 A curated list of awesome real-world adversarial examples resources

pytorch implementation of Parametric Noise Injection for adversarial defense

Machine Learning Attack Series

Code for the paper: Adversarial Training Against Location-Optimized Adversarial Patches. ECCV-W 2020.

Code for the paper "Consistency Regularization for Certified Robustness of Smoothed Classifiers" (NeurIPS 2020)

Learnable Boundary Guided Adversarial Training (ICCV2021)

GARNET: Reduced-Rank Topology Learning for Robust and Scalable Graph Neural Networks

Adversarial Ranking Attack and Defense, ECCV, 2020.