Over on YouTube the Linus Tech Tips channel has recently released a video about the HackRF titled "It’s TOO Easy to Accidentally Do Illegal Stuff with This". Linus Tech Tips is an extremely popular computer technology YouTube channel. The HackRF is a popular transmit capable software defined radio that was released about 10 years ago. The portapack is an add-on for the HackRF that allows the HackRF to be used as a handheld device, and when combined with the Mayhem firmware, it enables easy access to some controversial tools that could get a user into a lot of legal trouble very fast.

In the video Linus, whose team is based in Canada, mentions that they decided to purchase the HackRF and similar devices because of the Canadian government's plan to ban various RF tools, including the Flipper Zero and HackRF.

Linus then discusses and demonstrates "van eck phreaking" with TempestSDR, showing how he can use the HackRF to recover the video from a PC monitor wirelessly. He then goes on to demonstrate how the Portapack can be used to jam a wireless GoPro camera transmitting over WiFi. 

Finally, Linus discusses the legality and morality of such devices being available on the market.

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.