I tested the day 1 when Nano Banana Pro was released and it worked. It still works today for Nano Banana 2.
I didn't post this anywhere because I (arrogantly) thought saying it publicly would make the internet worse. But it was pure arrogancy: if I came up with this the first day then of course other millions of programmers did too.
Never released it, but it was obvious to most people in the SD community that denoising using a diffusion model was a relatively trivial means to beat most steganographic watermarks.
In my tests the image looks clearly distinct. In other words, if you can tell the difference then it isn’t a good test.
Eventually it won’t matter when image generation is cheap. But few self-host today and few are willing to pay unsubsidized prices, so the vast majority are using the Gemini, OpenAI, and Midjourney. If all 3 adopted SynthID, only a small fraction would use something else.
If social media platforms started banning images with these watermarks seems like they'd be stripped out overnight.
Set up as a ComfyUI workflow that does a few things: it tries SDXL, Flux, and a couple of different denoising methods at the lowest possible strength (progressively incrementing) to avoid changing the image too much, while also running a SynthID check each time, and repeating this in a loop until the watermark is essentially gone.
At the same time, you’d probably want to add some kind of threshold based on a perceptual hash aka the maximum perceptual quality difference you’re willing to accept.
(i'm sure there are countless bypasses out there, but please don't use something like this)
Can it be used to create something like nutritional labels for synthetic content? 10% synthetic text, 30 synthetic images.
Your reality was 15% synthetic today (75% mega corp, 25% open-weight neocloud).