Rather than marking AI generated images, they add metadata to camera-generated images, and use cryptographic signatures to prove the metadata is genuine. Just like adding a watermark, removing a watermark this way gives an image that looks basically identical to the original, at least to a human eye.Ĭoming at the problem from the opposite direction, some companies are working on ways to prove that an image came from a camera (“content authenticity”). For instance, if a watermark is encoded in the least important bits of an image, someone could remove it by simply setting all the least important bits to 0, or to a random value (1 or 0), or to a value automatically predicted based on neighboring pixels. The person who wants to remove a watermark isn’t limited to common edits, but can directly manipulate the image file. They must be robust against someone who knows about the watermark and wants to eliminate it. However, proposals for AI watermarking must pass a tougher challenge. There are more sophisticated watermarking proposals that are robust to a wider variety of common edits. However, this method has one clear flaw: rotating or resizing the image is likely to accidentally destroy the watermark. If that pattern repeats across the whole image, the watermark is even robust to cropping. By manipulating those least-important bits, a watermarking program can create a pattern that viewers won’t see, but a watermarking-detecting program will. Each pixel of an image is represented by a certain number of bits, and some of them make more of a perceptual difference than others. This kind of watermark is visible and is slightly challenging to remove since it requires some photo editing skills.īut to a computer it’s obvious that they are different by a single bit: #93c47d vs 93c57d. Stock image sites often overlay text on an image that renders it mostly useless for publication. One kind of watermark is already common for digital images. So far most have proven easy to remove, and it’s likely that future schemes will have similar problems. Unfortunately, watermarking schemes are unlikely to work. Or it could involve swapping words for synonyms in a predictable way so that the meaning is unchanged, but a program could readily determine the text was generated by an AI. For instance, this could involve taking an image and subtly changing many pixels in a way that’s undetectable to the eye but detectable to a computer program. One common proposal is that big companies should incorporate watermarks into the outputs of their AIs.

It would help people avoid endlessly arguing with bots online, or believing what a fake image purports to show. It would be nice if there were some way to reliably distinguish AI-generated content from human-generated content. Generative AI allows people to produce piles upon piles of images and words very quickly.

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