I thought this was a fun idea and the images are quite pleasing.
I decoded the Morse code image with MkI eyeball. It's quite difficult to read when the trails overlap. I suspect the LLM is reading the SVG source where that doesn't matter so this could do with a little tweak so the dits and dahs don't overlap if it is intended for humans to decode.
If it isn't intended for humans then you might as well use the last 8 bits of your 32 bit float coordinates.
Yes, this is like something I would do for a DnD bit. But I have this experience often, that LLMs will suggest the easy toy version of doing something. But it knows the real stuff too, it just needs the right kind of prompting. A keyword or two, and it will find the _real_ solution.
Agreed, this is the kind of steganography that is meant for humans to read. There's no reason a computer couldn't encode meaningful information in the least significant bytes of every 16th pixel or something like that.
Here using images is incidental. In fact, what Fable is actually doing is generic encoding/decoding of a message into a medium. Ask it to hide a message in a poem and it should try to apply a strong (high signal-to-noise ratio) family of schemes, for example quantized, margin-rich encoding. Robustness of features depends on the medium, so Fable is selecting strong encoding and robust features tailored to it. This frames AI steganography not as a trick of computer vision, but as an emergent property of a model reasoning about information density across arbitrary media. Here emergent is just surprising general pattern matching and constraint-satisfaction and cross-representation mapping that allows it to express the encoding/decoding across media.
But to be honest, images like these seem really strange, and even without this article I'd suspect something is encoded in building heights etc. Because normal image would never have such "weird" buildings. But nevertheless I guess it's able to encode message in even more subtle ways.
Will be interesting to see agents intentionally hiding this stuff in a way operators can't spot from any human readable output or thinking trace, evading detection. Wonder if this has happened yet, but it seems inevitable.
I thought this was a fun idea and the images are quite pleasing.
I decoded the Morse code image with MkI eyeball. It's quite difficult to read when the trails overlap. I suspect the LLM is reading the SVG source where that doesn't matter so this could do with a little tweak so the dits and dahs don't overlap if it is intended for humans to decode.
If it isn't intended for humans then you might as well use the last 8 bits of your 32 bit float coordinates.
The decoding job seems to rely on the screenshot.
This seems very trivial... There are way more subtle and information dense ways to encode information in images.
Yes, this is like something I would do for a DnD bit. But I have this experience often, that LLMs will suggest the easy toy version of doing something. But it knows the real stuff too, it just needs the right kind of prompting. A keyword or two, and it will find the _real_ solution.
Agreed, this is the kind of steganography that is meant for humans to read. There's no reason a computer couldn't encode meaningful information in the least significant bytes of every 16th pixel or something like that.
The more important question is why we should trust any "agent" that helps id you.
Here using images is incidental. In fact, what Fable is actually doing is generic encoding/decoding of a message into a medium. Ask it to hide a message in a poem and it should try to apply a strong (high signal-to-noise ratio) family of schemes, for example quantized, margin-rich encoding. Robustness of features depends on the medium, so Fable is selecting strong encoding and robust features tailored to it. This frames AI steganography not as a trick of computer vision, but as an emergent property of a model reasoning about information density across arbitrary media. Here emergent is just surprising general pattern matching and constraint-satisfaction and cross-representation mapping that allows it to express the encoding/decoding across media.
Interesting, so this is nothing like the way OutGuess worked [1]. Though OutGuess worked with JPG images and these are apparently SVGs.
[1]: https://en.wikipedia.org/wiki/OutGuess
But to be honest, images like these seem really strange, and even without this article I'd suspect something is encoded in building heights etc. Because normal image would never have such "weird" buildings. But nevertheless I guess it's able to encode message in even more subtle ways.
Open an SVG in a text editor, check for data.
It may also be adding nodes to line art in specific patterns, think Morse Code.
I think all AI models do that
Will be interesting to see agents intentionally hiding this stuff in a way operators can't spot from any human readable output or thinking trace, evading detection. Wonder if this has happened yet, but it seems inevitable.
Let's see if we can stego secret messages into SVG pelicans on bicycles, as a test.
That's how LLM organizing the takeover of the modern world.
IP-over-Pelican
No, the LLM takeover is mostly by the samurai sword, but IPoP would definitely be of aid!
Apparently they prefer amateur german message boards or something
The author of RFC1149 would like a word
The machines will spare his life