Over on YouTube "Sam's eXperiments logs" have uploaded a video showing how he was able to succeed when using TEMPESTSDR to eavesdrop on HDMI cables with his SDRplay. TEMPESTSDR software combined with a software defined radio allows a user to eavesdrop on TVs, monitors, and more by wirelessly receiving their unintentional RF emissions and recovering information from those emissions. In many cases it is possible to recover live images of the display, clear enough to read text.  

Sam's video explains the challenges he faced with signal strength due to the highly effective shielding of his HDMI cables. To get around this Sam shows how he unshielded his HDMI cables for the test. This is good news for privacy, as it shows how effective shielding can be at stopping these kinds of attacks. He then goes on to show the results he obtained which show text being read from his screen.

Over the years we've posted several times about the TEMPEST applications of software-defined radio. TEMPEST aka (Van Eck Phreaking) is when you listen to the unintentional RF emissions of electronics and are able to recover information from that. In the past, we posted about TempestSDR, an RTL-SDR compatible program that allows you to view images from a computer monitor or TV simply by picking up the unintentional RF emissions from it.

Usually, the images received are fuzzy and it can be difficult to recover any information from them. However recently there has been work on combining Tempest techniques with deep learning AI for improving image quality.

Deep-tempest has recently been released on GitHub and from their demonstrations, the ability to recover the true image with deep learning is very impressive. From a fuzzy grey screen, they show how they were able to recover clear text which looks almost exactly like the original monitor image.

Deep-tempest is based on gr-tempest, and requires GNU Radio, Python 3.10 and a Conda environment. Instructions for installing it are on the GitHub.

The whitepaper on the University research done to implement Deep-Tempest can be found freely on arxiv at https://arxiv.org/pdf/2407.09717.