Entries Tagged "videos"

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A Video Screen That Is Also a Camera

Amazing:

Researchers from ETH Zurich in Switzerland, however, managed to create a new type of pixel that can simultaneously do both. This hypercharged pixel, called a Fourier pixel, can generate and sense arbitrary light fields and tap into a pixel’s full potential for carrying information by manipulating light’s intensity, oscillation phases, and polarization. The team reported its findings in a paper published yesterday in Nature.

We are one step closer to 1984 technology:

The telescreen received and transmitted simultaneously. Any sound that Winston made, above the level of a very low whisper, would be picked up by it; moreover, so long as he remained within the field of vision which the metal plaque commanded, he could be seen as well as heard. There was of course no way of knowing whether you were being watched at any given moment.

Paper.

Posted on July 15, 2026 at 7:04 AM • View Comments

Adm. Grace Hopper’s 1982 NSA Lecture Has Been Published

The “long lost lecture” by Adm. Grace Hopper has been published by the NSA. (Note that there are two parts.)

It’s a wonderful talk: funny, engaging, wise, prescient. Remember that talk was given in 1982, less than a year before the ARPANET switched to TCP/IP and the internet went operational. She was a remarkable person.

Listening to it, and thinking about the audience of NSA engineers, I wonder how much of what she’s talking about as the future of computing—miniaturization, parallelization—was being done in the present and in secret.

Posted on August 29, 2024 at 11:58 AM • View Comments

New Research in Detecting AI-Generated Videos

The latest in what will be a continuing arms race between creating and detecting videos:

The new tool the research project is unleashing on deepfakes, called “MISLnet”, evolved from years of data derived from detecting fake images and video with tools that spot changes made to digital video or images. These may include the addition or movement of pixels between frames, manipulation of the speed of the clip, or the removal of frames.

Such tools work because a digital camera’s algorithmic processing creates relationships between pixel color values. Those relationships between values are very different in user-generated or images edited with apps like Photoshop.

But because AI-generated videos aren’t produced by a camera capturing a real scene or image, they don’t contain those telltale disparities between pixel values.

The Drexel team’s tools, including MISLnet, learn using a method called a constrained neural network, which can differentiate between normal and unusual values at the sub-pixel level of images or video clips, rather than searching for the common indicators of image manipulation like those mentioned above.

Research paper.

Posted on July 29, 2024 at 7:02 AM • View Comments

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Sidebar photo of Bruce Schneier by Joe MacInnis.