Security and Privacy Risk Simulator for Machine Learning (arXiv:2312.17667)

A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning. TPAMI, 2024.

A comprehensive toolbox for model inversion attacks and defenses, which is easy to get started.

A curated list of resources for model inversion attack (MIA).

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".

[CVPR-2023] Re-thinking Model Inversion Attacks Against Deep Neural Networks

[ICML 2023] "On Strengthening and Defending Graph Reconstruction Attack with Markov Chain Approximation"

reveal the vulnerabilities of SplitNN

Unofficial pytorch implementation of paper: Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures

[ICML 2023] On Strengthening and Defending Graph Reconstruction Attack with Markov Chain Approximation

[KDD 2022] "Bilateral Dependency Optimization: Defending Against Model-inversion Attacks"

Research into model inversion on SplitNN

📄 [Talk] OFFZONE 2022 / ODS Data Halloween 2022: Black-box attacks on ML models + with use of open-source tools

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

[NeurIPS 2024] "Pseudo-Private Data Guided Model Inversion Attacks"

Implementation of the model inversion attack on the Gated-Recurrent-Unit neural network