Deep-Tempest

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Deep-tempest is an open-source AI tool that uses deep learning to restore TEMPEST images. It improves the quality of images spied from unintended electromagnetic emanations, specifically from HDMI signals.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is deep-tempest?

Deep-tempest extends the original gr-tempest project, also known as Van Eck Phreaking, by integrating deep learning techniques to significantly enhance the quality of spied images. This tool focuses on recovering visual information from unintended electromagnetic emanations, particularly those originating from HDMI cables. By applying advanced deep learning architectures like DRUNet, deep-tempest can reduce the Character Error Rate from 90% in the unmodified gr-tempest to less than 30%, making the recovered text much more legible. The project includes open-sourced code and a comprehensive dataset of synthetic and real captured images for research, training, and evaluation, supporting both Python 3.10 and 3.12 environments with Conda or Pyenv + venv setups.

Best used for

Ideal for security researchers and developers who need to improve the quality of images captured from electromagnetic emanations, recover text from spied HDMI signals, and conduct advanced security research. Especially valuable for those working with signal processing and deep learning in the context of TEMPEST attacks.

Common actions

restore images

enhance image quality

analyze electromagnetic signals

conduct security research

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Capabilities

Key features

Deep learning image restoration
TEMPEST image enhancement
HDMI signal eavesdropping
Character Error Rate reduction
Open-source dataset
Python 3.10/3.12 compatibility

Target Audience

developerdata scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What is the primary improvement deep-tempest offers over the original gr-tempest?

Deep-tempest significantly improves image quality by using deep learning, reducing the Character Error Rate from 90% in the original gr-tempest to less than 30%. This makes recovered text from electromagnetic emanations much more readable and useful for analysis.

What kind of data is available with the deep-tempest project?

The project open-sources a ~7GB dataset containing both synthetic and real captured images. These images, at 1600x900 resolution, are used for experiments, training, and evaluation of the deep learning model, providing comprehensive resources for researchers.

What are the system requirements for setting up deep-tempest?

Deep-tempest requires Python 3.10 or 3.12, and has been tested on Ubuntu 22.04.5 LTS and 24.04.2 LTS. It supports CUDA 12.4 (with PyTorch/Torchvision for CUDA 12.1) and requires Tesseract OCR, with setup options for Conda or Pyenv + venv.

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