Must-read Papers on Textual Adversarial Attack and Defense
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Updated
Aug 9, 2024 - Python
Must-read Papers on Textual Adversarial Attack and Defense
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).
A list of awesome resources for adversarial attack and defense method in deep learning
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.
Provably defending pretrained classifiers including the Azure, Google, AWS, and Clarifai APIs
CVPR 2022 Workshop Robust Classification
Certified defense to adversarial examples using CROWN and IBP. Also includes GPU implementation of CROWN verification algorithm (in PyTorch).
[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
[ICML 2024] Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models
Adversarial attacks on Deep Reinforcement Learning (RL)
Adversarial Distributional Training (NeurIPS 2020)
😎 A curated list of awesome real-world adversarial examples resources
Machine Learning Attack Series
pytorch implementation of Parametric Noise Injection for adversarial defense
Code for the paper: Adversarial Training Against Location-Optimized Adversarial Patches. ECCV-W 2020.
GARNET: Reduced-Rank Topology Learning for Robust and Scalable Graph Neural Networks
Learnable Boundary Guided Adversarial Training (ICCV2021)
Code for the paper "Consistency Regularization for Certified Robustness of Smoothed Classifiers" (NeurIPS 2020)
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