Alternatives and similar repositories for adp

Users that are interested in adp are comparing it to the libraries listed below

• point0bar1 / ebm-defense
  PyTorch implementation of BPDA+EOT attack to evaluate adversarial defense with an EBM
  ☆25Updated 4 years ago
• ethz-spylab / diffusion_denoised_smoothing
  Certified robustness "for free" using off-the-shelf diffusion models and classifiers
  ☆41Updated 2 years ago
• nmndeep / revisiting-at
  [NeurIPS 2023] Code for the paper "Revisiting Adversarial Training for ImageNet: Architectures, Training and Generalization across Threa…
  ☆38Updated 6 months ago
• cassidylaidlaw / perceptual-advex
  Code and data for the ICLR 2021 paper "Perceptual Adversarial Robustness: Defense Against Unseen Threat Models".
  ☆55Updated 3 years ago
• zjfheart / Geometry-aware-Instance-reweighted-Adversarial-Training
  the paper "Geometry-aware Instance-reweighted Adversarial Training" ICLR 2021 oral
  ☆59Updated 4 years ago
• ml-postech / robust-evaluation-of-diffusion-based-purification
  [ICCV 2023 Oral] Official implementation of "Robust Evaluation of Diffusion-Based Adversarial Purification"
  ☆25Updated last year
• imrahulr / adversarial_robustness_pytorch
  Unofficial implementation of the DeepMind papers "Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples…
  ☆97Updated 3 years ago
• JinyiW / GuidedDiffusionPur
  ☆57Updated last year
• 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
• YuxinWenRick / canary-in-a-coalmine
  ☆34Updated last year
• watml / fast-wasserstein-adversarial
  Implementation of Wasserstein adversarial attacks.
  ☆23Updated 4 years ago
• NYU-DICE-Lab / circumventing-concept-erasure
  ☆20Updated last year
• wzekai99 / DM-Improves-AT
  Code for the paper "Better Diffusion Models Further Improve Adversarial Training" (ICML 2023)
  ☆138Updated last year
• hannxu123 / fair_robust
  ☆11Updated 3 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
• LijieFan / AdvCL
  [NeurIPS 2021] “When does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning?”
  ☆48Updated 3 years ago
• val-iisc / GAMA-GAT
  Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses, NeurIPS Spotlight 2020
  ☆27Updated 4 years ago
• tml-epfl / adv-training-corruptions
  On the effectiveness of adversarial training against common corruptions [UAI 2022]
  ☆30Updated 3 years ago
• fra31 / evaluating-adaptive-test-time-defenses
  ☆23Updated last year
• xherdan76 / A-Unified-Approach-to-Interpreting-and-Boosting-Adversarial-Transferability
  A Unified Approach to Interpreting and Boosting Adversarial Transferability (ICLR2021)
  ☆29Updated 3 years ago
• whj363636 / Self-Ensemble-Adversarial-Training
  SEAT
  ☆20Updated last year
• alinlab / consistency-adversarial
  Consistency Regularization for Adversarial Robustness (AAAI 2022)
  ☆53Updated 3 years ago
• P2333 / SCORE
  A Self-Consistent Robust Error (ICML 2022)
  ☆68Updated last year
• val-iisc / DAJAT
  Official Code for Efficient and Effective Augmentation Strategy for Adversarial Training (NeurIPS-2022)
  ☆16Updated 2 years ago
• dedeswim / vits-robustness-torch
  Code for the paper "A Light Recipe to Train Robust Vision Transformers" [SaTML 2023]
  ☆52Updated 2 years ago
• HanshuYAN / CIFS
  CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection
  ☆20Updated 3 years ago
• cvlab-columbia / SelfSupDefense
  ☆30Updated 3 years ago
• jinghuichen / AWM
  Github repo for One-shot Neural Backdoor Erasing via Adversarial Weight Masking (NeurIPS 2022)
  ☆15Updated 2 years ago
• davidstutz / confidence-calibrated-adversarial-training
  Implementation of Confidence-Calibrated Adversarial Training (CCAT).
  ☆45Updated 4 years ago
• imrahulr / hat
  Helper-based Adversarial Training: Reducing Excessive Margin to Achieve a Better Accuracy vs. Robustness Trade-off
  ☆32Updated 3 years ago