Alternatives and similar repositories for Fast-Machine-Unlearning

Users that are interested in Fast-Machine-Unlearning are comparing it to the libraries listed below

• ayushkumartarun / zero-shot-unlearning
  Official repo of the paper Zero-Shot Machine Unlearning accepted in IEEE Transactions on Information Forensics and Security
  ☆44Updated 2 years ago
• DingfanChen / RelaxLoss
  Official implementation of "RelaxLoss: Defending Membership Inference Attacks without Losing Utility" (ICLR 2022)
  ☆48Updated 3 years ago
• vikram2000b / bad-teaching-unlearning
  Official repo of the paper Can Bad Teaching Induce Forgetting? Unlearning in Deep Networks using an Incompetent Teacher accepted in AAAI …
  ☆37Updated last year
• Machine-Learning-Security-Lab / mia_prune
  Membership Inference Attacks and Defenses in Neural Network Pruning
  ☆27Updated 3 years ago
• OPTML-Group / Unlearn-Sparse
  [NeurIPS23 (Spotlight)] "Model Sparsity Can Simplify Machine Unlearning" by Jinghan Jia*, Jiancheng Liu*, Parikshit Ram, Yuguang Yao, Gao…
  ☆80Updated last year
• meghdadk / SCRUB
  ☆47Updated last year
• alewarne / MachineUnlearning
  Code related to the paper "Machine Unlearning of Features and Labels"
  ☆71Updated last year
• AdityaGolatkar / SelectiveForgetting
  ☆58Updated 5 years ago
• lmgraves / AmnesiacML
  Methods for removing learned data from neural nets and evaluation of those methods
  ☆37Updated 4 years ago
• zhuohangli / GGL
  A pytorch implementation of the paper "Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakage".
  ☆59Updated 2 years ago
• inspire-group / MIAdefenseSELENA
  [USENIX Security 2022] Mitigating Membership Inference Attacks by Self-Distillation Through a Novel Ensemble Architecture
  ☆17Updated 3 years ago
• cleverhans-lab / unrolling-sgd
  code release for "Unrolling SGD: Understanding Factors Influencing Machine Unlearning" published at EuroS&P'22
  ☆22Updated 3 years ago
• liuyugeng / baadd
  Code for Backdoor Attacks Against Dataset Distillation
  ☆35Updated 2 years ago
• LINs-lab / FedTHE
  [ICLR 2023] Test-time Robust Personalization for Federated Learning
  ☆53Updated 2 years ago
• vsingh-group / LCODEC-deep-unlearning
  Code for CVPR22 paper "Deep Unlearning via Randomized Conditionally Independent Hessians"
  ☆25Updated 3 years ago
• daiqing98 / FedCIL
  Code for ICLR 2023 Paper Better Generative Replay for Continual Federated Learning
  ☆31Updated 2 years ago
• yonsei-sslab / MIA
  🔒 Implementation of Shokri et al(2016) "Membership Inference Attacks against Machine Learning Models"
  ☆35Updated 3 years ago
• orientino / lira-pytorch
  Likelihood Ratio Attack (LiRA) in PyTorch
  ☆15Updated 6 months ago
• Jimmy-di / camouflage-poisoning
  Camouflage poisoning via machine unlearning
  ☆17Updated 2 months ago
• illidanlab / FedRBN
  [AAAI'23] Federated Robustness Propagation: Sharing Robustness in Heterogeneous Federated Learning
  ☆27Updated 2 years ago
• cchoquette / membership-inference
  Code for the paper: Label-Only Membership Inference Attacks
  ☆66Updated 4 years ago
• MinChen00 / UnlearningLeaks
  Official implementation of "When Machine Unlearning Jeopardizes Privacy" (ACM CCS 2021)
  ☆49Updated 3 years ago
• Sungwon-Han / FEDX
  ☆73Updated 3 years ago
• bboylyg / RNP
  Reconstructive Neuron Pruning for Backdoor Defense (ICML 2023)
  ☆39Updated last year
• royson / fedl2p
  [NeurIPS'23] FedL2P: Federated Learning to Personalize
  ☆23Updated last year
• AISafety-HKUST / Backdoor_Safety_Tuning
  Backdoor Safety Tuning (NeurIPS 2023 & 2024 Spotlight)
  ☆26Updated 10 months ago
• csdongxian / ANP_backdoor
  Codes for NeurIPS 2021 paper "Adversarial Neuron Pruning Purifies Backdoored Deep Models"
  ☆58Updated 2 years ago
• vrt1shjwlkr / AAAI21-MIA-Defense
  Code for AAAI 2021 Paper "Membership Privacy for Machine Learning Models Through Knowledge Transfer"
  ☆11Updated 4 years ago
• AdrienBenamira / membership_inference_attack
  Implementation of the paper : "Membership Inference Attacks Against Machine Learning Models", Shokri et al.
  ☆58Updated 6 years ago
• abbottyanginchina / Awesome-Federated-Unlearning
  Awesome Federated Unlearning (FU) Papers (Continually Update)
  ☆98Updated last year