invisible-watermark VS stable-diffusion

I'm not sure your online tool is working. I tried it with the watermarked example image from https://github.com/ShieldMnt/invisible-watermark, and your tool returned that it did not detect a watermark:

The public availability and quality of LLMs and stable diffusion have been an unprecedented disaster for spam mitigation largely because there is no effective way to determine if this content was created and posted by a human being. Particularly with text content, the amount of information present is so small that I don't believe there is a way to definitively analyze it and concretely say whether or not it was generated by an LLM. The only potential way to do so that I can think of would be to check every comment against the output of each LLM service provider, but that's a futile endeavor because you can go back to inserting typos and substitutions, reorder the text or omit some of it, mash multiple outputs together, or even self-host an LLM and skip all the bullshit from the start. At least the images and videos being created by stable diffusion can be watermarked reasonably well.

i found an article but i don't understand it... is there any video tutorial of anything?

Creating invisible watermark encoder (see https://github.com/ShieldMnt/invisible-watermark)...

Tools to add this kind of watermarking are publicly available or could be reimplemented by in house developers if they don't like FOSS licenses.

Most major AI generators embed invisible watermarks into the images so that they can detect them and avoid training on generated imagery later. I know Stable Diffusion uses this python library to do it: https://github.com/ShieldMnt/invisible-watermark I haven't bothered to look up others but they have similar steps.

It uses the same library as Stable Diffusion (https://github.com/ShieldMnt/invisible-watermark) without giving credit in its github repository which does not even contain the sources of its 3 lines of code. This watermark doesn't protect anything, it would be necessary that the robots that retrieve the images from the internet make the effort to read the watermark to not add them in their dataset (best case scenario, totally utopian). The repository is suspicious and could be a way to install malware.

- Stable diffusion uses https://github.com/ShieldMnt/invisible-watermark by default unless you check "Do not add watermark to images" in settings

"This script incorporates an invisible watermarking of the outputs, to help viewers identify the images as machine-generated."

I'm using a modified noise schedule (Karras et al, arXiv:2206.00364) taken from the LAION Discord user's fork (here). With that schedule, from their testing and my own, k_heun seems to perform about 3x better than others at equivalent steps (each step takes about 2x longer, but it's still a win). Also it performs well even with as low as 7 steps. I'd be surprised if euler was far superior since from my understanding, heun is basically an improved version of it.