emidan19/deep-tempest
Restoration for TEMPEST images using deep-learning
This tool helps cybersecurity professionals and researchers analyze unintended electromagnetic emanations from HDMI cables to reconstruct what's displayed on a screen. By inputting these raw electromagnetic signals, it generates a clearer, more readable image of the screen content than traditional methods, enabling improved analysis of potential data leakage.
669 stars. No commits in the last 6 months.
Use this if you need to recover visual information from electromagnetic signals emitted by display cables, especially when traditional signal processing yields unreadable or highly degraded images.
Not ideal if your goal is to analyze network traffic or capture digital data streams directly from a secure connection.
Stars
669
Forks
93
Language
Python
License
—
Category
Last pushed
May 28, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/emidan19/deep-tempest"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
cszn/KAIR
Image Restoration Toolbox (PyTorch). Training and testing codes for DPIR, USRNet, DnCNN, FFDNet,...
gabrieleilertsen/hdrcnn
HDR image reconstruction from a single exposure using deep CNNs
INVOKERer/DeepRFT
The code for 'Intriguing Findings of Frequency Selection for Image Deblurring' and 'Deep...
soumik12345/MIRNet
Tensorflow implementation of MIRNet for Low-light image enhancement
VinAIResearch/blur-kernel-space-exploring
Exploring Image Deblurring via Blur Kernel Space (CVPR'21)