Alternatives and similar repositories for N-FGSM

Users that are interested in N-FGSM are comparing it to the libraries listed below

• tml-epfl / adv-training-corruptions
  On the effectiveness of adversarial training against common corruptions [UAI 2022]
  ☆30Updated 3 years ago
• LTS4 / hold-me-tight
  Source code of "Hold me tight! Influence of discriminative features on deep network boundaries"
  ☆21Updated 3 years ago
• goldblum / AdversariallyRobustDistillation
  Pytorch implementation of Adversarially Robust Distillation (ARD)
  ☆59Updated 6 years ago
• davidstutz / confidence-calibrated-adversarial-training
  Implementation of Confidence-Calibrated Adversarial Training (CCAT).
  ☆45Updated 5 years ago
• fra31 / robust-finetuning
  Code relative to "Adversarial robustness against multiple and single $l_p$-threat models via quick fine-tuning of robust classifiers"
  ☆19Updated 2 years ago
• liuchen11 / AdversaryLossLandscape
  On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them [NeurIPS 2020]
  ☆36Updated 4 years ago
• imrahulr / hat
  Helper-based Adversarial Training: Reducing Excessive Margin to Achieve a Better Accuracy vs. Robustness Trade-off
  ☆33Updated 3 years ago
• MadryLab / smoothed-vit
  Certified Patch Robustness via Smoothed Vision Transformers
  ☆42Updated 3 years ago
• cassidylaidlaw / perceptual-advex
  Code and data for the ICLR 2021 paper "Perceptual Adversarial Robustness: Defense Against Unseen Threat Models".
  ☆55Updated 3 years ago
• VITA-Group / Alleviate-Robust-Overfitting
  [ICLR 2021] "Robust Overfitting may be mitigated by properly learned smoothening" by Tianlong Chen*, Zhenyu Zhang*, Sijia Liu, Shiyu Chan…
  ☆47Updated 3 years ago
• jh-jeong / smoothmix
  Code for the paper "SmoothMix: Training Confidence-calibrated Smoothed Classifiers for Certified Robustness" (NeurIPS 2021)
  ☆21Updated 2 years ago
• pralab / ImageNet-Patch
  ☆40Updated last year
• microsoft / denoised-smoothing
  Provably defending pretrained classifiers including the Azure, Google, AWS, and Clarifai APIs
  ☆97Updated 4 years ago
• BorealisAI / mma_training
  Code for the paper "MMA Training: Direct Input Space Margin Maximization through Adversarial Training"
  ☆34Updated 5 years ago
• ShawnXYang / AT_HE
  ☆35Updated 4 years ago
• UCSC-VLAA / AdvXL
  [CVPR 2024] This repository includes the official implementation our paper "Revisiting Adversarial Training at Scale"
  ☆20Updated last year
• tml-epfl / understanding-fast-adv-training
  Understanding and Improving Fast Adversarial Training [NeurIPS 2020]
  ☆95Updated 3 years ago
• tml-epfl / sharpness-vs-generalization
  A modern look at the relationship between sharpness and generalization [ICML 2023]
  ☆43Updated last year
• cihangxie / SmoothAdversarialTraining
  Smooth Adversarial Training
  ☆68Updated 4 years ago
• kyleliang919 / Uncovering-the-Connections-BetweenAdversarial-Transferability-and-Knowledge-Transferability
  code for ICML 2021 paper in which we explore the relationship between adversarial transferability and knowledge transferability.
  ☆17Updated 2 years ago
• yangarbiter / robust-local-lipschitz
  A Closer Look at Accuracy vs. Robustness
  ☆88Updated 4 years ago
• locuslab / robust_union
  [ICML'20] Multi Steepest Descent (MSD) for robustness against the union of multiple perturbation models.
  ☆26Updated last year
• VITA-Group / Sparsity-Win-Robust-Generalization
  [ICLR 2022] "Sparsity Winning Twice: Better Robust Generalization from More Efficient Training" by Tianlong Chen*, Zhenyu Zhang*, Pengjun…
  ☆39Updated 3 years ago
• dedeswim / vits-robustness-torch
  Code for the paper "A Light Recipe to Train Robust Vision Transformers" [SaTML 2023]
  ☆52Updated 2 years ago
• IBM / model-sanitization
  Codes for reproducing the results of the paper "Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness" published at IC…
  ☆27Updated 5 years ago
• uclaml / RayS
  RayS: A Ray Searching Method for Hard-label Adversarial Attack (KDD2020)
  ☆56Updated 4 years ago
• LijieFan / AdvCL
  [NeurIPS 2021] “When does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning?”
  ☆48Updated 3 years ago
• ethz-spylab / realistic-adv-examples
  Code for the paper "Evading Black-box Classifiers Without Breaking Eggs" [SaTML 2024]
  ☆21Updated last year
• harshays / inputgradients
  Do input gradients highlight discriminative features? [NeurIPS 2021] (https://arxiv.org/abs/2102.12781)
  ☆13Updated 2 years ago
• inspire-group / proxy-distributions
  [ICLR 2022 official code] Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness?
  ☆29Updated 3 years ago