## **PoC** [The code](https://github.com/ffhibnese/Model-Inversion-Attack-ToolBox) Defense algorithm scripts are in `./defense_scripts/`. To train a model with defense strategies, run: `python defensescripts/<DEFENSEMETHOD>.py` Training logs are saved to `./results/<DEFENSE_METHOD>/<DEFENSE_METHOD>.log` by default. To evaluate defense effectiveness, attack the model with: `python defensescripts/<DEFENSEMETHOD><ATTACKMETHOD>.py Attack outcomes are recorded in `./results/<DEFENSE_METHOD>_<ATTACK_METHOD>` by default. The framework also contains a adversarial toolkit - which can be found [here]() ## **Details** By assuming anattacker will gain access to the pre-trained models via white or black box methods, this toolkit will allow the defender to apply defenses a model,  to prevent them from reconstructing high-fidelity data that closely aligns with the private training data. [Paper](https://arxiv.org/abs/2402.04013) ### ATT&CK Matrix