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.

Video demo

Watch a video of the full system in operation below:

Cite this work (TODO)

Data

The data used can is available at this dropbox link within a ZIP file (~7GB). After unzipping, you will find synthetic and real captured images used for experiments, training, and evaluation during the work. These images consists of 1600x900 resolution with the SDR's center frequency at the third pixel-rate harmonic (324 MHz).

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 in this repository (even if you have already installed gr-tempest at a different location). After this, run the following grc files flowgraphs to activate the hierblocks:

• binary_serializer.grc
• FFT_autocorrelate.grc
• FFT_crosscorrelate.grc
• Keep_1_in_N_frames.grc

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