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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Security and Privacy Risk Simulator for Machine Learning (arXiv:2312.17667)
| Date | Stars |
|---|---|
| 2026-07-31 | 431 |
| 2026-08-04 | 431 |
| 2026-08-06 | 431 |
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<!-- Title: AIJack Description: AIJack is a fantastic framework demonstrating the security risks of machine learning and deep learning, such as Model Inversion, poisoning attack, and membership inference attack. Author: Hideaki Takahashi --> # AIJack: Security and Privacy Risk Simulator for Machine Learning <div align="left"> <img src="https://badgen.net/github/stars/Koukyosyumei/AIjack?color=green"> <img src="https://badgen.net/github/forks/Koukyosyumei/AIjack"> <img src="https://badgen.net/github/watchers/Koukyosyumei/AIjack"> <img src="https://static.pepy.tech/badge/aijack"> <img src="https://img.shields.io/github/languages/code-size/Koukyosyumei/AIJack"> <img src="https://app.codacy.com/project/badge/Grade/529a33e6523e4bf2a79c26e815a070f4"/> <img src="https://img.shields.io/github/license/Koukyosyumei/AIJack"> </div> <h1 align="center"> <br> <img src="docs/source/_static/logo_wide_png.png" alt="" width=1000> <br> <br> </h1> # What is AIJack? AIJack is an easy-to-use open-source simulation tool for testing the security of your AI system against hijackers. It provides advanced security techniques like *Differential Privacy*, *Homomorphic Encryption*, *K-anonymity* and *Federated Learning* to guarantee protection for your AI. With AIJack, you can test and simulate defenses against various attacks such as *Poisoning*, *Model Inversion*, *Backdoor*, and *Free-Rider*. We support more than 30 state-of-the-art methods. For more information, check our [paper](https://arxiv.org/abs/2312.17667) and [documentation](https://koukyosyumei.github.io/AIJack/) and start securing your AI today with AIJack. # Installation You can install AIJack with `pip`. AIJack requires Boost and pybind11. ``` apt install -y libboost-all-dev pip install -U pip pip install "pybind11[global]" pip install aijack ``` If you want to use the latest-version, you can directly install from GitHub. ``` pip install git+https://github.com/Koukyosyumei/AIJack ``` We also provide [Dockerfile](Dockerfile). # Quick Start We briefly introduce the overview of AIJack. ## Features - All-around abilities for both attack & defense - PyTorch-friendly design - Compatible with scikit-learn - Fast Implementation with C++ backend - MPI-Backend for Federated Learning - Extensible modular APIs ## Basic Interface ### Python API For standard machine learning algorithms, AIJack allows you to simulate attacks against machine learning models with `Attacker` APIs. AIJack mainly supports PyTorch or sklearn models. ```Python # abstract code attacker = Attacker(target_model) result = attacker.attack() ``` For instance, we can implement Poisoning Attack against SVM implemented with sklearn as follows. ```Python from aijack . attack import Poison_attack_sklearn attacker = Poison_attack_sklearn(clf , X_train , y_train) malicious_data , log = attacker.attack(initial_data , 1, X_valid , y_valid) ``` For distributed learning such as Federated Learning and Split Learning, AIJack offers four basic APIs: `Client`, `Server`, `API`, and `Manager`. `Client` and `Server` represent each client and server within each distributed learning scheme. You can execute training by registering the clients and servers to `API` and running it. `Manager` gives additional abilities such as attack, defense, or parallel computing to `Client`, `Server` or `API` via `attach` method. ```Python # abstract code client = [Client(), Client()] server = Server() api = API(client, server) api.run() # execute training c_manager = ClientManagerForAdditionalAbility(...) s_manager = ServerManagerForAdditionalAbility(...) ExtendedClient = c_manager.attach(Client) ExtendedServer = c_manager.attach(Server) extended_client = [ExtendedClient(...), ExtendedClient(...)] extended_server = ExtendedServer(...) api = API(extended_client, extended_server) api.run() # execute training ``` For example, the bellow code implements the scenario where the server in Federated Learning tries to st
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:5e1b4119cde2fe6b, llm:Repository description and README state: 'Security and Privacy Risk Simulator for Machine Learning', supports attacks (Model Inversion, Poisoning, Membership Inference, Backdoor, Free-Rider) and defenses (Differential Privacy, Homomorphic Encryption, K-anonymity, Federated Learning). Topics: adversarial-attacks, adversarial-examples, adversarial-machine-learning, differential-privacy, federated-learning, homomorphic-encryption, membership-inference, model-inversion-attacks, poisoning-attacks
matched fp:5e1b4119cde2fe6b, llm:Repository description and README state: 'Security and Privacy Risk Simulator for Machine Learning', supports attacks (Model Inversion, Poisoning, Membership Inference, Backdoor, Free-Rider) and defenses (Differential Privacy, Homomorphic Encryption, K-anonymity, Federated Learning). Topics: adversarial-attacks, adversarial-examples, adversarial-machine-learning, differential-privacy, federated-learning, homomorphic-encryption, membership-inference, model-inversion-attacks, poisoning-attacks
matched fp:5e1b4119cde2fe6b, llm:Repository description and README state: 'Security and Privacy Risk Simulator for Machine Learning', supports attacks (Model Inversion, Poisoning, Membership Inference, Backdoor, Free-Rider) and defenses (Differential Privacy, Homomorphic Encryption, K-anonymity, Federated Learning). Topics: adversarial-attacks, adversarial-examples, adversarial-machine-learning, differential-privacy, federated-learning, homomorphic-encryption, membership-inference, model-inversion-attacks, poisoning-attacks