pfl-research
AIJack
| pfl-research | AIJack | |
|---|---|---|
| 1 | 11 | |
| 193 | 330 | |
| 23.8% | - | |
| 8.4 | 7.3 | |
| 5 days ago | 5 days ago | |
| Python | C++ | |
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
pfl-research
AIJack
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Protect your AI with AIJack - an easy-to-use open-source simulation tool for testing the security of your AI system against hijackers
AIJack is easy to use and can help you secure your AI system quickly. Check our documentation for more information and start securing your AI today with AIJack. Don't wait for a hijacker to compromise your AI - take action today and protect your system with AIJack.
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How should I manage and develop my open-source project?
I have developed one OSS tool (AIJack), and I would like to ask how I manage it and where I should focus.
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AIJack: I built an OSS framework for the attack and defense against Machine Learning
I want to share my project, AIJack, a security and privacy risk simulator for machine learning. Many papers show that machine learning is vulnerable to cyber-attacks and privacy violations. For example, hackers can reconstruct private training data from the trained model. To simulate such risks, AIJack allows you to experiment with various combinations of more than 30 attack and defense mechanisms, such as Model Inversion, Poisoning Attack, Evasion Attack, Federated Learning, Split Learning, Differential Privacy, and Homomorphic Encryption.
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Privacy-Preserving Machine Learning with AIJack - 1: Federated Learning on PyTorch
Next, we will implement FedAVG, one of the most representative methods of Federated Learning. We use AIJack, an OSS, to simulate machine learning algorithms' security and privacy risks. AIJack supports both single-process and MPI as its backend.
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[P] Let's Hijack AI! Security and Privacy Risk Simulator for Machine Learning
I have released v0.0.1-alpha of AIJack, an OSS framework to simulate various attacks and defenses against machine learning models. I have implemented more than 30 algorithms, such as Model Inversion, Poisoning Attack, Evasion Attack, Federated Learning, Split Learning, Differential Privacy, and Homomorphic Encryption. You can easily experiment with various combinations of attack and defense techniques. We will also support not only standard single-process but also MPI-backend.
I have developed a framework named AIJack to simulate various attacks against machine learning models, mainly based on PyTorch and sklearn. Currently, I have implemented more than 20 algorithms Federated Learning, Split Learning, Differential Privacy, Homomorphic Encryption, and other heuristic approaches. I am looking forward to your feedback!
- AIJack - Security and Privacy Risk Simulator for Machine Learning
- AIJack: Security and Privacy Risk Simulator for Machine Learning
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Let's hijack AI! Security and Privacy Risk Simulator for Machine Learning
I have developed AIJack, which allows you to assess the privacy and security risks of machine learning algorithms such as Model Inversion, Poisoning Attack and Evasion Attack. AIJack also provides various defense techniques like Federated Learning, Split Learning, Differential Privacy, Homomorphic Encryption, and other heuristic approaches. You can easily experiment with various combinations of attacks and defenses.
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Let's Hijack AI Security and Privacy Risk Simulator for Machine Learning
I have developed a framework named AIJack to simulate various attacks against machine learning models, mainly based on PyTorch and sklearn. Currently, I have implemented more than 20 algorithms! I am looking forward to your feedback!
code: https://github.com/Koukyosyumei/AIJack
documentation: https://koukyosyumei.github.io/AIJack/intro.html
What are some alternatives?
flower - Flower: A Friendly Federated Learning Framework
MetisFL - The first open Federated Learning framework implemented in C++ and Python.
FedML - FEDML - The unified and scalable ML library for large-scale distributed training, model serving, and federated learning. FEDML Launch, a cross-cloud scheduler, further enables running any AI jobs on any GPU cloud or on-premise cluster. Built on this library, FEDML Nexus AI (https://fedml.ai) is your generative AI platform at scale.
concrete - Concrete: TFHE Compiler that converts python programs into FHE equivalent
TextAttack - TextAttack 🐙 is a Python framework for adversarial attacks, data augmentation, and model training in NLP https://textattack.readthedocs.io/en/master/
mlattacks - Machine Learning Attack Series
awesome-machine-unlearning - Awesome Machine Unlearning (A Survey of Machine Unlearning)
adversarial-robustness-toolbox - Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams
federated-xgboost - Federated gradient boosted decision tree learning