Proof of Capture: Apple Reference Image, but open source and using steganography

fitzn

Here's a toy for embedding small text into images steganographically: https://github.com/fitzn/atrium

xg15

> For example, a screen attack still works: photograph a screen displaying an AI image and you get a signed photo of a fake. But it's always nice seeing big actors interested in addressing this problem. Yeah, very nice. So this whole idea basically doesn't work - but we get a new stealth way to embed metadata in an image that can be used for tracking... (And a new narrative why cameras need to have TPMs and locked-down firmware as well)

vzaliva

I expect in the near future all digital cameras to digitally sign the images they take. Even before AI slop, it was useful to avoid manual alterations. AI makes it all too easy, so it makes sense. However, this will certify only the original image. I think the missing part of this is additional layers of certification which allow some image editing (e.g., rotating, contrast, etc.) yet clearly document that the image was modified and link to the original image ID. Kind of like a signed git log.

treyd

I don't understand why few people are pointing out the obvious vulnerability here that you can control the wires going into the photosensor controller and pretend that the photosensor is capturing whatever image you want. I imagine it's not exactly trivial to do this, but a grad student with an FPGA could probably figure it out.

Lammy

In the future, people willingly surveil themselves 24/7 with cryptographic proof, because fake images and video will be so good that it will be the only way to prove what one didn't do. Total Information Awareness achieved :D

Retr0id

> we sign a perceptual hash (pHash) of the image rather than an exact pixel checksum Perceptual hashes are non-cryptographic. There are certainly collision attacks, but what about preimages? A preimage would completely break this scheme. This paper demonstrates second-preimage attacks against PhotoDNA and PDQ: https://eprint.iacr.org/2021/1531.pdf