ffhibnese
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diff --git a/‎.vscode/settings.json‎
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diff --git a/‎README.md‎
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diff --git a/‎attack_scripts/README.md‎
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diff --git a/‎attack_scripts/brep.py‎
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diff --git a/‎attack_scripts/c2fmi.py‎
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diff --git a/‎attack_scripts/deepinversion.py‎
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@@ -109,7 +109,10 @@ cache/
 *.pt
 *.pth
 *.tar
+*.pkl
+*.tar.gz
 results/*
+/results*
 # checkpoints/*/*
 dataset/*/*
 
@@ -122,5 +125,8 @@ nohup.txt
 # !dataset/celeba/README.md
 /test.py
 /test.ipynb
-/test
+/test*
 .vscode
+test*.py
+test*.sh
+checkpoints_v2/
@@ -1,9 +1,14 @@
 {
     "python.analysis.extraPaths": [
         "./src"
-    ],
+    ],  
     "[python]": {
-        "editor.defaultFormatter": "ms-python.black-formatter"
+        "editor.defaultFormatter": "ms-python.black-formatter",
+        "editor.formatOnSave": true,
+
+        "editor.formatOnType": true
     },
-    "python.formatting.provider": "none"
+    "black-formatter.args": [
+        "-S"
+    ],
 }
@@ -1,4 +1,4 @@
-# 🔥Model Inversion Attack ToolBox v1.0🔥
+# 🔥Model Inversion Attack ToolBox v2.0🔥
 
 ![Python 3.10](https://img.shields.io/badge/python-3.10-DodgerBlue.svg?style=plastic)
 ![Pytorch 2.0.1](https://img.shields.io/badge/pytorch-2.0.1-DodgerBlue.svg?style=plastic)
@@ -13,23 +13,14 @@
 [Xuan Wang](https://faculty.hitsz.edu.cn/wangxuan),
 [Shu-Tao Xia](https://www.sigs.tsinghua.edu.cn/xst/main.htm)
 
-Welcome to **MIA**! This repository is a comprehensive open-source Python benchmark for model inversion attacks, which is well-organized and easy to get started. It includes uniform implementations of advanced and representative model inversion methods, formulating a unified and reliable framework for a convenient and fair comparison between different model inversion methods.
+Welcome to **MIA**! This repository is a comprehensive open-source Python benchmark for model inversion attacks, which is well-organized and easy to get started. It includes uniform implementations of advanced and representative model inversion methods, formulating a unified and reliable framework for a convenient and fair comparison between different model inversion methods. Our repository is continuously updated in **https://github.com/ffhibnese/Model-Inversion-Attack-ToolBox**.
 
 
 If you have any concerns about our toolbox, feel free to contact us at qiuyixiang@stu.hit.edu.cn, yuhongyao@stu.hit.edu.cn, and fang-h23@mails.tsinghua.edu.cn.
 
 Also, you are always welcome to contribute and make this repository better! 
 
 
-### :construction: MIA v2.0 is coming soon
-We are already in the second development stage where the following updates will be implemented soon.
-- More recently emerging attacks
-- Representative defense algorithms
-- MI attacks on graph and language modalities
-- Able to train your generative model
-- Better refactor code with `trainer`
-- A package that can be installed with pip
-
 ## :rocket: Introduction
 
 **Model inversion attack** is an emerging powerful private data theft attack, where a malicious attacker is able to reconstruct data with the same distribution as the training dataset of the target model. 
@@ -68,7 +59,6 @@ The reason why we developed this toolbox is that the research line of **MI** suf
 
 |Method|Paper|Publication|Key Characteristics|
 |:-:|:-:|:-:|:-:|
-|[DPSGD](./src/modelinversion/defense/DP/)|Deep Learning with Differential Privacy|[CCS'2016](https://dl.acm.org/doi/abs/10.1145/2976749.2978318)|add noise on gradient|
 |[ViB / MID](./src/modelinversion/defense/Vib/)|Improving Robustness to Model Inversion Attacks via Mutual Information Regularization|[AAAI'2021](https://ojs.aaai.org/index.php/AAAI/article/view/17387)| variational method, mutual information, special loss function|
 |[BiDO](./src/modelinversion/defense/BiDO/)|Bilateral Dependency Optimization: Defending Against Model-inversion Attacks|[KDD'2022](https://dl.acm.org/doi/abs/10.1145/3534678.3539376)|special loss function|
 |[TL](./src/modelinversion/defense/TL/)|Model Inversion Robustness: Can Transfer Learning Help?|[CVPR'2024](https://openreview.net/forum?id=nW0sCc3LLN&nesting=2&sort=date-desc)|transfer learning|
@@ -88,7 +78,17 @@ conda activate MIA
 pip install -r requirements.txt
 ```
 
-## :page_facing_up: Datasets and Model Checkpoints
+## :page_facing_up: Preprocess Datasets and Pre-trained Models
+
+See [here](./docs/datasets.md) for details to preprocess datasets. 
+
+We have released pre-trained target models and evaluation models in the `checkpoints_v2.0` of [Google Drive](https://drive.google.com/drive/folders/1ko8zAK1j9lTSF8FMvacO8mCKHY9evG9L?usp=sharing).
+
+<!-- ## :racehorse: Run Examples
+
+See [here](./docs/) for details. -->
+
+<!-- ## :page_facing_up: Datasets and Model Checkpoints
 - For datasets, you can download them according to the file with detailed instructions placed in `./dataset/<DATASET_NAME>/README.md`. 
 - For pre-trained models, we prepare all the related model weights files in the following link.   
 Download pre-trained models [here](https://drive.google.com/drive/folders/1ko8zAK1j9lTSF8FMvacO8mCKHY9evG9L) and place them in `./checkpoints/`. The detailed file path structure is shown in `./checkpoints_structure.txt`.
@@ -121,7 +121,7 @@ python defense_scripts/<DEFENSE_METHOD>_<ATTACK_METHOD>.py
 ```
 and attack results will be produced in `./results/<DEFENSE_METHOD>_<ATTACK_METHOD>` by default.
 
-For more information, you can read [here](./defense_scripts/README.md).
+For more information, you can read [here](./defense_scripts/README.md). -->
 
 ## 📔 Citation
 **If you find our work helpful for your research, please kindly cite our paper:**