From 426b9adfbc5b59a58e62c3759c0ba4822e67ccf4 Mon Sep 17 00:00:00 2001 From: Hideaki Takahashi Date: Sun, 7 Apr 2024 13:02:44 +0900 Subject: [PATCH] add links to paper and contribution guide --- README.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 5474c1f1..adf69e22 100644 --- a/README.md +++ b/README.md @@ -29,7 +29,7 @@ # 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 [documentation](https://koukyosyumei.github.io/AIJack/) and start securing your AI today with 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 @@ -237,6 +237,10 @@ Below you can find a list of papers and books that either use or extend AIJack. - Huang, Shiyuan. A General Framework for Model Adaptation to Meet Practical Constraints in Computer Vision. Diss. Columbia University, 2024. - Liu, Can, Jin Wang, and Dongyang Yu. "RAF-GI: Towards Robust, Accurate and Fast-Convergent Gradient Inversion Attack in Federated Learning." arXiv preprint arXiv:2403.08383 (2024). +# Contribution + +AIJack welcomes contributions of any kind. If you'd like to address a bug or propose a new feature, please refer to [our guide](docs/source/contribution.rst). + # Contact welcome2aijack[@]gmail.com