I’ve been spending time thinking about audio and video manipulation recently. As content assets become 100% digital and AI/ML proliferates into creation use-cases, there are large opportunities for the good (think gaming, simulation, at-scale content creation, etc.) that can be unlocked by the manipulation of these assets. Whether it’s in music, art, or even writing.

Pandora’s box will inevitably open, and out will come the manipulation of digital assets for net-negative solutions. This is what interests me most.

If we believe that the fake news of today lies within headlines, the fake news of tomorrow could expand to phone calls, videos, and more. Our analog and digital worlds are going to continually converge until distinction and identification becomes a blurred line. I’m looking for companies hoping to solve this problem.

The low-hanging fruit is scalable audio manipulation detection. Whether we view this as political risk (hacked phone calls -> manipulated audio), legal risk (falsified conversations as evidence), or social risk (there are endless possibilities), the ability to detect the tampering/manipulation/creation will need to commercially scale with the actual creation side.

Adobe, the leader in fake photos via photoshop, has shown an early ability to falsify audio with just 20 minutes of data. They claim they are already working on watermarking for detection purposes, but we all know this will be cracked.

Meanwhile UC Berkeley has built CycleGAN, which can falsify a variety of visual assets using image to image translation.

Stanford’s graphics lab has also done re-enactment to falsify speaking in video via its Face2Face research.

In early research, I’ve found a fair amount of academic papers focusing on image manipulation detection as well as older commercial tools ranging from Verifeyed to Belkasoft and more.

I’m still sorting through what the future business of manipulation detection is for audio and video, but I’m not the only person who has noticed the potential impact on our future. Companies like Clarifai have shown the power of utilizing an API model in order to scale machine learning across commercial use-cases of all sorts, and I believe that this problem will need a similar solution.

If you have thoughts on this space, I’d love to hear them either via email or twitter.