811
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
this post was submitted on 28 Aug 2023
811 points (99.0% liked)
Selfhosted
39677 readers
814 users here now
A place to share alternatives to popular online services that can be self-hosted without giving up privacy or locking you into a service you don't control.
Rules:
-
Be civil: we're here to support and learn from one another. Insults won't be tolerated. Flame wars are frowned upon.
-
No spam posting.
-
Posts have to be centered around self-hosting. There are other communities for discussing hardware or home computing. If it's not obvious why your post topic revolves around selfhosting, please include details to make it clear.
-
Don't duplicate the full text of your blog or github here. Just post the link for folks to click.
-
Submission headline should match the article title (don’t cherry-pick information from the title to fit your agenda).
-
No trolling.
Resources:
- selfh.st Newsletter and index of selfhosted software and apps
- awesome-selfhosted software
- awesome-sysadmin resources
- Self-Hosted Podcast from Jupiter Broadcasting
Any issues on the community? Report it using the report flag.
Questions? DM the mods!
founded 1 year ago
MODERATORS
Not well versed in the field, but understand that large tech companies which host user-generated content match the hashes of uploaded content against a list of known bad hashes as part of their strategy to detect and tackle such content.
Could it be possible to adopt a strategy like that as a first-pass to improve detection, and reduce the compute load associated with running every file through an AI model?
It's more than just basic hash matching because it has to catch content even if it's been resized, cropped, reduced in quality (lower JPEG quality with more artifacts), colour balance change, etc.
Well, we have hashing algorithms that do exactly that, like phash for example.
Definitely. A lot of the good algorithms used by big services are proprietary though, unfortunately.
Can you point me to some of them? I'm quite interested in visual hashing.
Microsoft's PhotoDNA is probably the most well-known. Every major service that has user-generated content uses it. Last I checked, it wasn't open-source. It was built for detecting CSAM, but it's really just a general-purpose similarity hashing algorithm.
Meta has some algorithms that are open-source: https://about.fb.com/news/2019/08/open-source-photo-video-matching/
Google has CSAI Match for hash-matching of videos and Google Content Safety API for classification of new content, but both are proprietary.
There's better approaches than hashing. For comparing images I am calculating "distance" in tensors between them. This can match even when compression artifacts are involved or the images are slightly altered.
Ah, of course - that's unfortunate, but thanks for the pointer.