Watermarking an image to mark is one’s own is something that has value across countless domains, but these days it’s more difficult than just adding a logo in the corner. Steg.AI lets creators embed a nearly invisible watermark using deep learning, defying the usual “resize and resave” countermeasures.

Ownership of digital assets has had a complex few years, what with NFTs and AI generation shaking up what was a fairly low-intensity field before. If you really need to prove the provenance of a piece of media, there have been ways of encoding that data into images or audio, but these tend to be easily defeated by trivial changes like saving the PNG as a JPEG. More robust watermarks tend to be visible or audible, like a plainly visible pattern or code on the image.

An invisible watermark that can easily be applied, just as easily detected, and which is robust against transformation and re-encoding is something many a creator would take advantage of. IP theft, whether intentional or accidental, is rife online and the ability to say “look, I can prove I made this” — or that an AI made it — is increasingly vital.Steg.AI has been working on a deep learning approach to this problem for years, as evidenced by this 2019 CVPR paper and the receipt of both Phase I and II SBIR government grants. Co-founders (and co-authors) Eric Wengrowski and Kristin Dana worked for years before that in academic research; Dana was Wengrowski’s PhD advisor.

While Wengrowski noted that though they have made numerous advances since 2019, the paper does show the general shape of their approach.

“Imagine a generative AI company creates an image and Steg watermarks it before delivering it to the end user,” he wrote in an email to TechCrunch. “The end user might post the AI-generated image on social media. Copies of the deployed image will still contain the Steg.AI watermark, even if the image is resized, compressed, screenshotted, or has its traditional metadata deleted. Steg.AI watermarks are so robust that they can be scanned from an electronic display or printout using an iPhone camera.”Although they understandably did not want to provide the exact details of the process, it works more or less like this: instead of having a static watermark that must be awkwardly layered over a piece of media, the company has a matched pair of machine learning models that customize the watermark to the image. The encoding algorithm identifies the best places to modify the image in such a way that people won’t perceive it, but that the decoding algorithm can pick out easily — since it uses the same process, it knows where to look.