This is protptype tool of paper: Modelobfuscator: Obfuscating Model Information to Protect Deployed ML-Based Systems on ISSTA2023.
(0) Download the Docker Image:
docker pull anonymousauthor000/code275:v3.1
Note that if it cause permission errors, please try:
sudo docker pull anonymousauthor000/code2536:v2
(1) Enter the environment:
docker run -i -t anonymousauthor000/code275:v3.1 /bin/bash
Note that if it cause permission errors, please try:
docker run -i -t anonymousauthor000/code2536:v2 /bin/bash
Enter the project:
cd code275/
(2) Activate the conda environment:
conda activate code275
(0) Download the code:
git clone https://github.com/AnonymousAuthor000/code275.git
cd code275
(1) The dependency can be found in environment.yml. To create the conda environment:
conda env create -f environment.yml
conda activate code275
Install the Flatbuffer:
conda install -c conda-forge flatbuffers
(if no npm) install the npm:
sudo apt-get install npm
Install the jsonrepair:
npm install -g jsonrepair
Note that the recommend version of gcc and g++ is 9.4.0.
(2) Download the source code of the TensorFlow. Here we test our tool on v2.9.1.
wget https://github.com/tensorflow/tensorflow/archive/refs/tags/v2.9.1.zip
Unzip the file:
unzip v2.9.1
(3) Download the Bazel:
wget https://github.com/bazelbuild/bazelisk/releases/download/v1.14.0/bazelisk-linux-amd64
chmod +x bazelisk-linux-amd64
sudo mv bazelisk-linux-amd64 /usr/local/bin/bazel
You can test the Bazel:
which bazel
It should return:
/usr/local/bin/bazel
(4) Configure the build:
cd tensorflow-2.9.1/
./configure
cd ..
You can use the default setting (just type Return/Enter for every option).
(5) Copy the configurations and script to the source code:
cp ./files/kernel_files/* ./tensorflow-2.9.1/tensorflow/lite/kernels/
cp ./files/build_files/build.sh ./tensorflow-2.9.1/
Note that you can mofify the maximal number of jobs in the 'build.sh' script. Here I set it as --jobs=14.
(1) Build the obfuscation model:
bash build_obf.sh
Note that you can modify the test model and obfuscation parameters in the script. The obfuscated model is saved as the 'obf_model.tflite'.