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Search results for python adversarial robustness

adversarial-robustness x

24 search results found

Auto Attack ⭐ 587

Code relative to "Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks"

Robustbench ⭐ 566

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

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

Self Adaptive Training ⭐ 104

Official implementation of the NeurIPS'2020 paper 'Self-Adaptive Training: beyond Empirical Risk Minimization'

Infobert ⭐ 81

[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

Pytorch implementation of our NeurIPS'20 *Oral* paper "DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles" https://papers.nips.cc/paper/2020/hash/3ad7c2ebb96

Featurescatter ⭐ 39

Feature Scattering Adversarial Training

Adversarial_robustness_pytorch ⭐ 37

Unofficial implementation of the DeepMind papers "Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples" & "Fixing Data Augmentation to Improve Adversarial Robustness" in PyTorch

Square Attack ⭐ 27

Square Attack: a query-efficient black-box adversarial attack via random search [ECCV 2020]

L_inf Dist Net ⭐ 24

This is the official github repo for training L_inf dist nets with high certified accuracy.

Lipschitz Neural Networks described in "Sorting Out Lipschitz Function Approximation" (ICML 2019).

Triple Wins ⭐ 22

[ICLR 2020] ”Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference“

Adversarial Attack and Defense in Deep Ranking, arXiv:2106.03614

Fab Attack ⭐ 18

Code for FAB-attack

Robust Residual Network ⭐ 15

Revisiting Residual Networks for Adversarial Robustness: An Architectural Perspective

Decoupled Kullback-Leibler Divergence Loss (DKL)

Helper-based Adversarial Training: Reducing Excessive Margin to Achieve a Better Accuracy vs. Robustness Trade-off

Robust Finetuning ⭐ 13

Code relative to "Adversarial robustness against multiple and single $l_p$-threat models via quick fine-tuning of robust classifiers"

PyTorch implementation of adversarial defenses [Fantastic Robustness Measures: The Secrets of Robust Generalization, NeurIPS 2023].

Reliable_gnn_via_robust_aggregation ⭐ 7

This repository contains the official implementation of the paper "Reliable Graph Neural Networks via Robust Aggregation" (NeurIPS, 2020).

Privacyattack_at_fl ⭐ 7

A privacy attack that exploits Adversarial Training models to compromise the privacy of Federated Learning systems.

Adversarial_robustness_zsl ⭐ 5

[ECCV 2020 AROW Workshop] A Deep Dive into Adversarial Robustness in Zero-Shot Learning

Sparseadversarialtraining ⭐ 5

Code for "Training Adversarially Robust Sparse Networks via Bayesian Connectivity Sampling" [ICML 2021]

Double Win Lth ⭐ 5

[ICML 2022] "Data-Efficient Double-Win Lottery Tickets from Robust Pre-training" by Tianlong Chen, Zhenyu Zhang, Sijia Liu, Yang Zhang, Shiyu Chang, Zhangyang Wang

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