Alternatives and similar repositories for NTL

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

• cleverhans-lab / dataset-inference
  [ICLR'21] Dataset Inference for Ownership Resolution in Machine Learning
  ☆32Updated 3 years ago
• HanxunH / CognitiveDistillation
  [ICLR2023] Distilling Cognitive Backdoor Patterns within an Image
  ☆36Updated 2 months ago
• KuofengGao / ASD
  [CVPR 2023] Backdoor Defense via Adaptively Splitting Poisoned Dataset
  ☆49Updated last year
• kaiwenzha / contrastive-poisoning
  [ICLR 2023, Spotlight] Indiscriminate Poisoning Attacks on Unsupervised Contrastive Learning
  ☆33Updated 2 years ago
• psandovalsegura / autoregressive-poisoning
  Code for the paper "Autoregressive Perturbations for Data Poisoning" (NeurIPS 2022)
  ☆20Updated last year
• conditionWang / Data_Centric_AI_IP_Protection
  This is the repository that introduces research topics related to protecting intellectual property (IP) of AI from a data-centric perspec…
  ☆23Updated 2 years ago
• jinghuichen / AWM
  Github repo for One-shot Neural Backdoor Erasing via Adversarial Weight Masking (NeurIPS 2022)
  ☆15Updated 3 years ago
• zibojia / RSLAD
  This is the official code for "Revisiting Adversarial Robustness Distillation: Robust Soft Labels Make Student Better"
  ☆45Updated 4 years ago
• kai-wen-yang / CD-VAE
  [NeurIPS 2021] "Class-Disentanglement and Applications in Adversarial Detection and Defense"
  ☆46Updated 4 years ago
• vsingh-group / LCODEC-deep-unlearning
  Code for CVPR22 paper "Deep Unlearning via Randomized Conditionally Independent Hessians"
  ☆25Updated 3 years ago
• Saehyung-Lee / cifar10_challenge
  Code for the CVPR 2020 article "Adversarial Vertex mixup: Toward Better Adversarially Robust Generalization"
  ☆13Updated 5 years ago
• ChaojianYu / Robust-Weight-Perturbation
  Implementation for <Robust Weight Perturbation for Adversarial Training> in IJCAI'22.
  ☆16Updated 3 years ago
• rkteddy / channel-Lipschitzness-based-pruning
  Source code for ECCV 2022 Poster: Data-free Backdoor Removal based on Channel Lipschitzness
  ☆34Updated 3 years ago
• LijieFan / AdvCL
  [NeurIPS 2021] “When does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning?”
  ☆48Updated 4 years ago
• bymavis / CAS_ICLR2021
  ☆58Updated 3 years ago
• inspire-group / tta_risk
  ☆14Updated 2 years ago
• lhfowl / adversarial_poisons
  ☆54Updated 4 years ago
• Jimmy-di / camouflage-poisoning
  Camouflage poisoning via machine unlearning
  ☆18Updated 6 months ago
• mo666666 / When-Adversarial-Training-Meets-Vision-Transformers
  Official implementation of "When Adversarial Training Meets Vision Transformers: Recipes from Training to Architecture" published at Neur…
  ☆37Updated last year
• Muzammal-Naseer / ATViT
  Official repository for "On Improving Adversarial Transferability of Vision Transformers" (ICLR 2022--Spotlight)
  ☆73Updated 3 years ago
• uchicago-sandlab / naturalbackdoors
  Code for identifying natural backdoors in existing image datasets.
  ☆15Updated 3 years ago
• xherdan76 / A-Unified-Approach-to-Interpreting-and-Boosting-Adversarial-Transferability
  A Unified Approach to Interpreting and Boosting Adversarial Transferability (ICLR2021)
  ☆31Updated 3 years ago
• TLMichael / Delusive-Adversary
  [NeurIPS 2021] Better Safe Than Sorry: Preventing Delusive Adversaries with Adversarial Training
  ☆32Updated 4 years ago
• Alan-Qin / Transfer_attack_RAP
  Boosting the Transferability of Adversarial Attacks with Reverse Adversarial Perturbation (NeurIPS 2022)
  ☆33Updated 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…
  ☆49Updated 4 years ago
• JunfengGo / Domain-Watermark
  ☆21Updated last year
• zjfheart / Geometry-aware-Instance-reweighted-Adversarial-Training
  the paper "Geometry-aware Instance-reweighted Adversarial Training" ICLR 2021 oral
  ☆59Updated 4 years ago
• ZFancy / IAD
  [ICLR 2022] Reliable Adversarial Distillation with Unreliable Teachers
  ☆22Updated 3 years ago
• jiangw-0 / LE_JCDP
  Unlearnable Examples Give a False Sense of Security: Piercing through Unexploitable Data with Learnable Examples
  ☆11Updated last year
• UCDvision / backdoor_transformer
  ☆33Updated 3 years ago