Security and Privacy Risk Simulator for Machine Learning (arXiv:2312.17667)
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Updated
May 10, 2024 - C++
Security and Privacy Risk Simulator for Machine Learning (arXiv:2312.17667)
Code for ML Doctor
Implementation of the Model Inversion Attack introduced with Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures (Fredrikson Et al.)
[ICML 2022 / ICLR 2024] Source code for our papers "Plug & Play Attacks: Towards Robust and Flexible Model Inversion Attacks" and "Be Careful What You Smooth For".
A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning. arXiv:2307.09218.
A curated list of resources for model inversion attack (MIA).
reveal the vulnerabilities of SplitNN
Bilateral Dependency Optimization: Defending Against Model-inversion Attacks
Unofficial pytorch implementation of paper: Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures
📄 [Talk] OFFZONE 2022 / ODS Data Halloween 2022: Black-box attacks on ML models + with use of open-source tools
Research into model inversion on SplitNN
[CVPR-2023] Re-thinking Model Inversion Attacks Against Deep Neural Networks
[ICML 2023] On Strengthening and Defending Graph Reconstruction Attack with Markov Chain Approximation
A comprehensive toolbox for model inversion attacks and defenses, which is easy to get started.
Implementation of "An Approximate Memory based Defense against Model Inversion Attacks to Neural Networks" and "MIDAS: Model Inversion Defenses Using an Approximate Memory System"
[CVPR-2023] Re-thinking Model Inversion Attacks Against Deep Neural Networks
[ICML 2023] "On Strengthening and Defending Graph Reconstruction Attack with Markov Chain Approximation"
Implementation of the model inversion attack on the Gated-Recurrent-Unit neural network
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