inspire-group / privacy-vs-robustnessLinks
Privacy Risks of Securing Machine Learning Models against Adversarial Examples
☆44Updated 5 years ago
Alternatives and similar repositories for privacy-vs-robustness
Users that are interested in privacy-vs-robustness are comparing it to the libraries listed below
Sorting:
- Code for the paper: Label-Only Membership Inference Attacks☆66Updated 4 years ago
- Code for Machine Learning Models that Remember Too Much (in CCS 2017)☆31Updated 7 years ago
- Code for the CSF 2018 paper "Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting"☆39Updated 6 years ago
- ☆26Updated 6 years ago
- ☆45Updated 5 years ago
- ☆32Updated last year
- ☆66Updated 6 years ago
- Implementation of the Model Inversion Attack introduced with Model Inversion Attacks that Exploit Confidence Information and Basic Counte…☆85Updated 2 years ago
- Prediction Poisoning: Towards Defenses Against DNN Model Stealing Attacks (ICLR '20)☆32Updated 4 years ago
- Code for Exploiting Unintended Feature Leakage in Collaborative Learning (in Oakland 2019)☆54Updated 6 years ago
- Attacking a dog vs fish classification that uses transfer learning inceptionV3☆71Updated 7 years ago
- KNN Defense Against Clean Label Poisoning Attacks☆12Updated 4 years ago
- ☆48Updated 4 years ago
- Code for the paper "ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models"☆84Updated 3 years ago
- Code for "Neural Network Inversion in Adversarial Setting via Background Knowledge Alignment" (CCS 2019)☆48Updated 5 years ago
- CVPR 2021 Official repository for the Data-Free Model Extraction paper. https://arxiv.org/abs/2011.14779☆72Updated last year
- Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks (RAID 2018)☆48Updated 6 years ago
- Craft poisoned data using MetaPoison☆53Updated 4 years ago
- Code for ML Doctor☆90Updated last year
- ABS: Scanning Neural Networks for Back-doors by Artificial Brain Stimulation☆51Updated 3 years ago
- Systematic Evaluation of Membership Inference Privacy Risks of Machine Learning Models☆127Updated last year
- The code is for our NeurIPS 2019 paper: https://arxiv.org/abs/1910.04749☆34Updated 5 years ago
- Code for "On the Trade-off between Adversarial and Backdoor Robustness" (NIPS 2020)☆17Updated 4 years ago
- ☆19Updated 2 years ago
- ☆26Updated 6 years ago
- ☆19Updated last year
- Example of the attack described in the paper "Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization"☆21Updated 5 years ago
- Code for Membership Inference Attack against Machine Learning Models (in Oakland 2017)☆197Updated 7 years ago
- [NeurIPS 2021] Source code for the paper "Qu-ANTI-zation: Exploiting Neural Network Quantization for Achieving Adversarial Outcomes"☆16Updated 3 years ago
- ☆21Updated 3 years ago