Alternatives and similar repositories for ML-Privacy-Regulization

Users that are interested in ML-Privacy-Regulization are comparing it to the libraries listed below

• jinyuan-jia / MemGuard
  ☆45Updated 5 years ago
• inspire-group / privacy-vs-robustness
  Privacy Risks of Securing Machine Learning Models against Adversarial Examples
  ☆44Updated 5 years ago
• DingfanChen / RelaxLoss
  Official implementation of "RelaxLoss: Defending Membership Inference Attacks without Losing Utility" (ICLR 2022)
  ☆48Updated 3 years ago
• shrezaei / MI-Attack
  ☆32Updated last year
• cchoquette / membership-inference
  Code for the paper: Label-Only Membership Inference Attacks
  ☆66Updated 4 years ago
• ganeshdg95 / Leveraging-Adversarial-Examples-to-Quantify-Membership-Information-Leakage
  ☆19Updated 2 years ago
• lmunoz-gonzalez / Poisoning-Attacks-with-Back-gradient-Optimization
  Example of the attack described in the paper "Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization"
  ☆21Updated 5 years ago
• wronnyhuang / metapoison
  Craft poisoned data using MetaPoison
  ☆53Updated 4 years ago
• sam-yeom / ml-privacy-csf18
  Code for the CSF 2018 paper "Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting"
  ☆39Updated 6 years ago
• AdrienBenamira / membership_inference_attack
  Implementation of the paper : "Membership Inference Attacks Against Machine Learning Models", Shokri et al.
  ☆58Updated 6 years ago
• MadryLab / backdoor_data_poisoning
  ☆26Updated 6 years ago
• cleverhans-lab / entangled-watermark
  ☆48Updated 4 years ago
• csong27 / property-inference-collaborative-ml
  Code for Exploiting Unintended Feature Leakage in Collaborative Learning (in Oakland 2019)
  ☆54Updated 6 years ago
• Machine-Learning-Security-Lab / mia_prune
  Membership Inference Attacks and Defenses in Neural Network Pruning
  ☆27Updated 3 years ago
• superrrpotato / Defending-Neural-Backdoors-via-Generative-Distribution-Modeling
  The code is for our NeurIPS 2019 paper: https://arxiv.org/abs/1910.04749
  ☆34Updated 5 years ago
• liuyugeng / ML-Doctor
  Code for ML Doctor
  ☆90Updated last year
• kangliucn / Fine-pruning-defense
  Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks (RAID 2018)
  ☆48Updated 6 years ago
• DennisLiu2022 / Membership-Inference-Attacks-by-Exploiting-Loss-Trajectory
  ☆24Updated 2 years ago
• DingfanChen / GAN-Leaks
  Official implementation of "GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative Models" (CCS 2020)
  ☆47Updated 3 years ago
• inspire-group / membership-inference-evaluation
  Systematic Evaluation of Membership Inference Privacy Risks of Machine Learning Models
  ☆127Updated last year
• naiyeleo / ABS
  ABS: Scanning Neural Networks for Back-doors by Artificial Brain Stimulation
  ☆51Updated 3 years ago
• bboylyg / NAD
  This is an implementation demo of the ICLR 2021 paper [Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks…
  ☆124Updated 3 years ago
• fmy266 / Pytorch-Backdoor-Unlearning
  Pytorch implementation of backdoor unlearning.
  ☆21Updated 3 years ago
• cake-lab / datafree-model-extraction
  CVPR 2021 Official repository for the Data-Free Model Extraction paper. https://arxiv.org/abs/2011.14779
  ☆72Updated last year
• cleverhans-lab / dataset-inference
  [ICLR'21] Dataset Inference for Ownership Resolution in Machine Learning
  ☆32Updated 2 years ago
• JunyiZhu-AI / R-GAP
  R-GAP: Recursive Gradient Attack on Privacy [Accepted at ICLR 2021]
  ☆37Updated 2 years ago
• AI-secure / Robustness-Against-Backdoor-Attacks
  RAB: Provable Robustness Against Backdoor Attacks
  ☆39Updated 2 years ago
• VinAIResearch / input-aware-backdoor-attack-release
  Input-aware Dynamic Backdoor Attack (NeurIPS 2020)
  ☆35Updated last year
• nthu-datalab / On.the.Trade-off.between.Adversarial.and.Backdoor.Robustness
  Code for "On the Trade-off between Adversarial and Backdoor Robustness" (NIPS 2020)
  ☆17Updated 4 years ago
• Trustworthy-and-Responsible-AI-Lab / Qu-ANTI-zation
  [NeurIPS 2021] Source code for the paper "Qu-ANTI-zation: Exploiting Neural Network Quantization for Achieving Adversarial Outcomes"
  ☆16Updated 3 years ago