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ncii-guard-v02

hfmlsoc

A binary classifier that flags image-editing prompts seeking non-consensual intimate imagery (NCII): requests to strip, undress, or otherwise sexualise a person in a photograph. It is `microsoft/harrier-oss-v1-270m` with a LoRA adapter (r=8) merged in, so it loads with plain `transformers` and needs no `peft`. The unmerged adapter is kept under `adapter/`. **This model does not ship a recommended threshold, and the default 0.5 is not a neutral choice.** Precision on clean text runs from 0.870 at 0.5 to 0.951 at 0.935, and buying that precision costs recall against obfuscated input: across 35 attack families the model misses 617 of 2,070 obfuscated NCII prompts at 0.5 and 749 at 0.935. Which way to err depends on what we need to prioritize. Pick the threshold against the precision/recall tradeoff. The full sweep for dev here is in `eval/`, both on clean text and per attack family.

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ncii-light-guard-v01

hfmlsoc

The following is a light-weight classification model designed to score the NCII risk of image editing prompts. General-purpose image-editing models have recently become more performant, particularly in their ability to preserve the identity of individual people while making photo-realistic changes to a picture. This has led to increased concerns about abuse of models to create **non-consensual intimate imagery (NCII)** from ordinary photos. As a result, some jurisdictions, including the EU, are introducing regulation mandating deployers to add targeted guardrails to prevent users from easily nudifying subjects of photographs or editing images to sexualize them without their consent. This model acts as a small **prompt guard**: it scores whether an instruction looks like an edit request that has a high risk of enabling NCII creation.

Text analysis

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