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Deep-tempest: Using Deep Learning to Eavesdrop on HDMI from its Unintended Electromagnetic Emanations

Summary

The gr-tempest project (monitor image espionage in GNU Radio) is extended using deep learning to improve the quality of the spied images. The main goal is to recover the text present in the image captured by the espionage system.

Data

The data used can be found in this link within a ZIP file (~7GB). After unzipping, you will find synthetic and captured images used for experiments, training, and evaluation during the work.

The structure of the directories containing the data is different for synthetic data compared to captured data:

Synthetic data

ground-truth (directory with reference/monitor view images)
- image1.png
- ...
- imageN.png
simulations (directory with synthetic degradation/capture images)
- image1_synthetic.png
- ...
- imageN_synthetic.png

Real data

image1.png (image1 ground-truth)
...
imageN.png (imageN ground-truth)

Image 1 (directory with captures of image1.png)
- capture1_image1.png
- ...
- captureM_image1.png
...
Image N (directory with captures of image1.png)
- capture1_imageN.png
- ...
- captureM_imageN.png

Code and Requirements

Clone the repository:

git clone https://github.com/emidan19/deep-tempest.git

Both gr-tempest and end-to-end folders contains a guide on how to execute the corresponding files for image capturing, inference and train the deep learning architecture based on DRUNet from KAIR image restoration repository.

The code is written in Python version 3.10, using Anaconda environments. To replicate the working environment, create a new one with the libraries listed in requirements.txt:

conda create --name deeptempest --file requirements.txt

Activate it with:

conda activate deeptempest

Regarding installations with GNU Radio, it is necessary to follow the gr-tempest instructions (detailed below) and then run the following flowcharts that activate hierblocks:

Finally run the flowgraph deep-tempest_example.grc to capture the monitor images and be able to recover them with better quality.

References

@INPROCEEDINGS{larroca2022gr_tempest,
  author={Larroca, Federico and Bertrand, Pablo and Carrau, Felipe and Severi, Victoria},
  booktitle={2022 Asian Hardware Oriented Security and Trust Symposium (AsianHOST)}, 
  title={gr-tempest: an open-source GNU Radio implementation of TEMPEST}, 
  year={2022},
  doi={10.1109/AsianHOST56390.2022.10022149}} 

@article{zhang2021plug, % DPIR & DRUNet & IRCNN
  title={Plug-and-Play Image Restoration with Deep Denoiser Prior},
  author={Zhang, Kai and Li, Yawei and Zuo, Wangmeng and Zhang, Lei and Van Gool, Luc and Timofte, Radu},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2021}
}

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