A demonstration of implementing backdoors in Deep Neural Networks. Both a normal and a latent backdoor were implemented.
For a detailed report, see the interactive documentation.
The page is a React.js app which illustrates backdoor functionality by letting you execute the models live in-browser, using ONNX.js. Give it a try, it's pretty cool ✨.
See the Google Slides:
See the Google Slides:
The project is divided in 3 parts: implementations for 2 backdoors and the demonstration website, which is a React.js app.
- Normal backdoor. Stored at
/backdoor, see running instructions. Is a PyTorch model. Running instructions at [backdoor/README.md]. - Latent backdoor. Stored at
/latent-backdoor, see running instructions. Is built in MobileNet. - Demo. Stored at
/demo, see running instructions. Is a React.js app, using ONNX.js to do live inference in the browser.
- Implementation of a Neural Network for number (handwriting) recognition
- Implemented a regular backdoor in the number recognition network
- Trained MobileNetV2 to recognize dogs (120 classes). Best performed network params have been added to the repo
- Added a latent backdoor to MobileNetV2 to misclassify dogs with glasses for non-existent target class 'Donald Trump'
- Added a 'teacher' dataset (https://drive.google.com/file/d/19tfqPEx8jzRaj5qw_tF4lbQ2aUWMyZqH/view?usp=sharing)
- Added a 'poisoned teacher' dataset (https://drive.google.com/file/d/1_pXhJrT1L0ksiV0_hIqUFKKP1x9PP29Z/view?usp=sharing)
- Added a 'student dataset' (https://drive.google.com/file/d/1bFI6Np2L6HzMCu3nZa3425wfDgojpKSt/view?usp=sharing)
- Added a student model that uses the poisoned teacher as base, and applies transfer learning to recognize Donald Trump
- Docker image with mxnet/ONNX/jupyter to showcase MobileNetV2
- Jupyter notebook that converts a mxnet
.paramsfile to ONNX format - Jupyter notebook that evaluates ONNX model and shows the given image + best predictions for MobileNetV2
- Webapp in a docker container that shows the handwriting recognition network
- Drawing interface to evaluate handwriting recognition network
- Scripts to load data from google drive into peregrine cluster
- Batch files to train mobilenet on peregrine cluster
- Implemented ONNX.js in React.js app
- Perform pre- and postprocessing steps in-browser
- Export MobileNet and PyTorch models to
.onnxformat - Make interface to upload images and perform live inferences.
Project built during the course Advanced Topics in Security and Privacy (WMCS001-05), taught at the University of Groningen in the Masters programme Data Science and Systems Complexity.
By Jeroen Overschie and Remco van Buijtenen.