ThreatExchange
pHash
| ThreatExchange | pHash | |
|---|---|---|
| 7 | 2 | |
| 1,123 | 529 | |
| 1.4% | - | |
| 9.2 | 0.0 | |
| 7 days ago | over 1 year ago | |
| C++ | C++ | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 only |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
ThreatExchange
- Meta Launches Open-Source Tool 'Hasher-Matcher-Actioner' To Prevent Spread of Terror Content
- GitHub - facebook/ThreatExchange: Share threat information with vetted partners
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Strengthening Our Efforts Against the Spread of Non-Consensual Intimate Images
It’s somewhere in the FAQ at https://stopncii.org/faq/, but It’s PDQ for photos (https://github.com/facebook/ThreatExchange/tree/main/pdq) and MD5 for videos. PDQ is resistant to some modifications (it focuses on the ones that come from regular usage, such as changing the format from gif to jpg, or a filter changing colors or brightness), but it’s not as resistant to modifications as you could get by training dedicated classifiers or other approaches that you might do with the original media or by storing more context, which StopNCII chose not to do.
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Stopncii.org – Client Side Hashing to Prevent Spread of NCII, Preserve Privacy
The FAQ (https://stopncii.org/faq/) mentions its using PDQ (https://github.com/facebook/ThreatExchange/tree/main/pdq) for images, and MD5 for videos. Companies would then use hashes to scan their platform.
Meta also has a post on it: https://about.fb.com/news/2021/12/strengthening-efforts-agai...
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Proposed illegal image detectors on devices are ‘easily fooled’
Notably, none of the the algorithms tested in the cited study are Apple's NeuralHash, or comparable algorithms. They look at aHash, pHash (plus a variant thereof), dHash, and and PDQ (used at Facebook for similar applications, apparently). The first 4 data to between 2004 and 2010; the last is more recent, but conceptually similar - the citation [0] for PDQ puts it in the same bucket of 'shallower, stricter, cheaper, faster' algorithms as the first four.
No one has proposed any of those as 'illegal image detectors'. Apple's NeuralHash may or may not be robust to the same or different perturbations but the cited study provides basically no new information to inform the conversation its press release wants to be a part of.
[0]: https://github.com/facebook/ThreatExchange/blob/main/hashing...
pHash
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pHash
Glad it's on Github here: https://github.com/aetilius/pHash
Github tends to outlive the homepage of some projects. I got worried when I read this:
> Copyright © 2008-2010
- PHash – the open source perceptual hash library
What are some alternatives?
vpv - Image viewer for image processing experts
meow_hash - Official version of the Meow hash, an extremely fast level 1 hash
digestpp - C++11 header-only message digest library
Phashion - Ruby wrapper around pHash, the perceptual hash library for detecting duplicate multimedia files
parallel-hashmap - A family of header-only, very fast and memory-friendly hashmap and btree containers.
robin-hood-hashing - Fast & memory efficient hashtable based on robin hood hashing for C++11/14/17/20
OpenHashTab - 📝 File hashing and checking shell extension