The Cost of Proving a Video

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There's a number of ways that we can go about addressing these. One thing you can do for cameras is to also include a depth sensor, which actually many phones do today and include the depth information with the pixels. And then recording it with an attested microphone. This works as long as there's no deep fake of the video as well. The other big limitation is the computational complexity in producing these proofs. Unfortunately, they're still quite expensive,. But due to the amazing work of people in this space, the cost of proving is going down dramatically.

In this bonus episode, Anna jumps back on the mic for a quick follow-up to Episode 279: Intro to zkpod.ai. Guest Daniel Kang describes a joint project he is doing with zkpod.ai - the Attested Audio Experiment. The objective is to prove that some piece of audio originates from a "real" (non-ai-generated) recording, even if this audio has been altered through edits or modifications. The goal is to differentiate between original "real" audio and the audio generated by zkpod.ai. In the future some iteration of this architecture could be used by content creators to distinguish deepfake audio from the real thing.

Here is the write up about the Attested Audio Experiment on Daniel Kang's blog.

Here is some of his other work as well:

Follow @zkpodai on twitter for more updates!