Must-read Papers on Textual Adversarial Attack and Defense
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Updated
Jul 12, 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"
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 Distributional Training (NeurIPS 2020)
pytorch implementation of Parametric Noise Injection for adversarial defense
Code for the paper: Adversarial Training Against Location-Optimized Adversarial Patches. ECCV-W 2020.
Learnable Boundary Guided Adversarial Training (ICCV2021)
Code for the paper "Consistency Regularization for Certified Robustness of Smoothed Classifiers" (NeurIPS 2020)
GARNET: Reduced-Rank Topology Learning for Robust and Scalable Graph Neural Networks
[IEEE TIP 2021] Self-Attention Context Network: Addressing the Threat of Adversarial Attacks for Hyperspectral Image Classification
Feature Separation and Recalibration (CVPR 2023 Highlights)
Source Code for 'SECurity evaluation platform FOR Speaker Recognition' released in 'Defending against Audio Adversarial Examples on Speaker Recognition Systems'
Adversarial Ranking Attack and Defense, ECCV, 2020.
[ECCV 2020] Pytorch codes for Open-set Adversarial Defense
Adversarial Attack and Defense in Deep Ranking, T-PAMI, 2024
Implementation of paper "Transferring Robustness for Graph Neural Network Against Poisoning Attacks".
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