Alternatives and similar repositories for ml-model-remember

Users that are interested in ml-model-remember are comparing it to the libraries listed below

• jinyuan-jia / MemGuard
  ☆45Updated 5 years ago
• csong27 / property-inference-collaborative-ml
  Code for Exploiting Unintended Feature Leakage in Collaborative Learning (in Oakland 2019)
  ☆53Updated 6 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
• inspire-group / privacy-vs-robustness
  Privacy Risks of Securing Machine Learning Models against Adversarial Examples
  ☆44Updated 5 years ago
• kangliucn / Fine-pruning-defense
  Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks (RAID 2018)
  ☆47Updated 6 years ago
• naiyeleo / ABS
  ABS: Scanning Neural Networks for Back-doors by Artificial Brain Stimulation
  ☆52Updated 3 years ago
• yashkant / model-inversion-attack
  Implementation of the Model Inversion Attack introduced with Model Inversion Attacks that Exploit Confidence Information and Basic Counte…
  ☆84Updated 2 years ago
• columbia / pixeldp
  ☆66Updated 5 years ago
• cchoquette / membership-inference
  Code for the paper: Label-Only Membership Inference Attacks
  ☆65Updated 3 years ago
• yziqi / adversarial-model-inversion
  Code for "Neural Network Inversion in Adversarial Setting via Background Knowledge Alignment" (CCS 2019)
  ☆47Updated 5 years ago
• SPIN-UMass / ML-Privacy-Regulization
  ☆26Updated 6 years ago
• sam-yeom / ml-privacy-csf18
  Code for the CSF 2018 paper "Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting"
  ☆37Updated 6 years ago
• hyhmia / BlindMI
  ☆23Updated 2 years ago
• ashafahi / inceptionv3-transferLearn-poison
  Attacking a dog vs fish classification that uses transfer learning inceptionV3
  ☆70Updated 7 years ago
• AhmedSalem2 / ML-Leaks
  Code for the paper "ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models"
  ☆85Updated 3 years ago
• 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
• liuyugeng / ML-Doctor
  Code for ML Doctor
  ☆91Updated 10 months ago
• csl-cqu / awesome-secure-federated-learning-papers
  ☆29Updated 2 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
• shrezaei / MI-Attack
  ☆31Updated 9 months ago
• jagielski / manip-ml
  Code for the IEEE S&P 2018 paper 'Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning'
  ☆53Updated 4 years ago
• AdrienBenamira / membership_inference_attack
  Implementation of the paper : "Membership Inference Attacks Against Machine Learning Models", Shokri et al.
  ☆60Updated 6 years ago
• cleverhans-lab / entangled-watermark
  ☆48Updated 4 years ago
• Machine-Learning-Security-Lab / mia_prune
  Membership Inference Attacks and Defenses in Neural Network Pruning
  ☆28Updated 2 years ago
• garrisongys / STRIP
  This is for releasing the source code of the ACSAC paper "STRIP: A Defence Against Trojan Attacks on Deep Neural Networks"
  ☆57Updated 7 months ago
• inspire-group / membership-inference-evaluation
  Systematic Evaluation of Membership Inference Privacy Risks of Machine Learning Models
  ☆126Updated last year
• AhmedSalem2 / Updates-Leak
  The code for our Updates-Leak paper
  ☆16Updated 4 years ago
• tribhuvanesh / prediction-poisoning
  Prediction Poisoning: Towards Defenses Against DNN Model Stealing Attacks (ICLR '20)
  ☆32Updated 4 years ago
• GeorgePisl / backdoor-attacks-based-on-deep-learning
  Implementation of Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning paper
  ☆20Updated 5 years ago
• bargavj / EvaluatingDPML
  This project's goal is to evaluate the privacy leakage of differentially private machine learning models.
  ☆135Updated 2 years ago