AIJack: Security and Privacy Risk Simulator for Machine Learning

AIJack allows you to assess the privacy and security risks of machine learning algorithms such as Model Inversion, Poisoning Attack, Evasion Attack, Free Rider, and Backdoor Attack. AIJack also provides various defense techniques like Differential Privacy, Homomorphic Encryption, and other heuristic approaches. In addition, AIJack provides APIs for many distributed learning schemes like Federated Learning and Split Learning. You can integrate many attack and defense methods into such collaborative learning with a few lines. We currently implement more than 30 state-of-arts methods.

https://github.com/Koukyosyumei/AIJack