4 ms·
if you tell it to generate the AI image with a black background you can visually see the synthid with a good enough monitor, it's just a repeating fuzzy pattern
by himata4113 5mo ago
if you tell it to generate the AI image with a black background you can visually see the synthid with a good enough monitor, it's just a repeating fuzzy pattern, nothing special.
I have found great success of getting rid of it by masking every 2nd pixel, regenerating missing pixels and then once again masking every 2nd pixel offset by 1.
Used an off the shelf model to fill in the pixels, but I also exported a depthmap first (before any alternations) and denoised it so generated masked pixels comform to the original content. The result was obviously not 100% perfect, but with more time and a model fine tuned for this specific use-case would be able to remove any kind of ai watermarking without too many issues.
- tantalor 5mo agoBut why
- teravor 5mo agoi wouldn't have any confidence in being able to remove a 0.5 bit watermark (presence/absence). what you see is probably a functional decoy.
- huflungdung 5mo ago[dead]
- deleted 5mo ago[deleted]
- yorwba 5mo agoYou can use the verification tool to check whether the watermark is still detectable and iterate until you successfully removed it . Of course if you need to regenerate the image with an unwatermarked image-generation model to remove it, it more or less still serves its purpose.
- tux3 5mo agoHow confident are you that the verification tool detects all of the watermark, and not just one layer? I would layer two watermarks and let the public remove the most visible one.
- soraminazuki 5mo agoWhat good does a totally imaginary AI watermark that'll never be revealed to the public even do?
- ndr 5mo agoIt might not be intended for the public. Similar to your printer fingerprinting: https://en.wikipedia.org/wiki/Printer_tracking_dots https://en.wikipedia.org/wiki/Printer_tracking_dots
- PxldLtd 5mo agoYou can reveal it later when you come up with a new mechanism and out all the fake images. Basically the first layer is first defence, second layer is cleanup when releasing a new mechanism. That way your generated images will always be identifiable eventually.
- unglaublich 5mo agoIt's to mark the images for exclusion during training.
- ZiiS 5mo agoA better way of looking at it is you only need to introduce a .5 bit error.
- userbinator 5mo agobut with more time and a model fine tuned for this specific use-case would be able to remove any kind of ai watermarking without too many issues. Always amusing to see AI used against itself.
- sigbeta 5mo ago[dead]
- m00dy 5mo agoIt’s definitely hackable, Some of our engineers worked on this long time ago https://deepwalker.xyz/blog/bypassing-synthid-in-gemini-photos https://deepwalker.xyz/blog/bypassing-synthid-in-gemini-phot...
- VortexLain 5mo agoConducting insurance fraud? What a usecase.
- unglaublich 5mo agoYou'd be surprised how many people use AI to commit such insurance fraud.
- DroneBetter 5mo agoit would be nice to show an amplified pixelwise difference between the before and after images
- noodlesUK 5mo agoI'm shocked that someone would write a blog post like this in which they openly admit to something that is widely understood to be fraud. Even if I'm sympathetic to why this individual chose to do this, and the technical side is interesting, I think the decision to just publicly tell a story in which you criminally defraud the villain is not a choice I'd ever make.
- himata4113 5mo agoIt appears that this company already does fraud so they're most likely comfortable with fraud. It seems normal in isolation, but from an outside lense it's crazy.
- m00dy 5mo agoSorry, but what you’re saying goes beyond the kind of free expression I would respect. Can you tell us what fraud you believe we’re committing, under what law, and based on what evidence? This blog post clearly shows that Deepwalker can break SynthID, which is a closed-source watermarking system.
- cryptoegorophy 5mo agoCan an image just be stretched or compressed a very tiny bit?
- deleted 5mo ago[deleted]
- dyauspitr 5mo agoSo it actually affects the quality of the image. Well, that sucks.
- unglaublich 5mo agoWhat would be the alternative? If you would store more information without affecting image quality, then compression would remove this information again while retaining the image quality. You will _have_ to alter the 'visible' image to store information.
- dyauspitr 5mo agoYeah I get that anything short of an alteration of the entire surface of the image is reversible. Still, the fact that it is visible sucks.
- himata4113 5mo agoa model trained specifically for this would not have accuracy loss ~99% identical if I had to guess I got to around 85%.
- neksn 5mo agoInterestingly, using AI Studio with the gemini-2.5-flash-image model and asking it to generate a "completely black image" fails with the message "Image recitation block," which has zero hits in Google.
- deleted 5mo ago[deleted]
- jmole 5mo ago"image recitation block" means they are blocking generation of images that already exist in their database (training data).
- neksn 5mo agoJust to confirm, what this means is that the training data contains one or multiple pitch black images, and their system is refusing to reproduce them verbatim?
- jmole 5mo agono insider knowledge here. my assumption is that the image hash matches a training data image. "all black" is a pretty easy hash to match.