NSFW Blocker For Gravity Forms
Block inappropriate image uploads in Gravity Forms by running NSFW detection in the visitor's browser before submission.
What it does
When you accept image uploads through a Gravity Forms Post Image field, you have no built-in way to stop visitors from submitting explicit or inappropriate content. NSFW Blocker for Gravity Forms adds a content check that runs before the form is submitted.
The check happens entirely in the visitor's browser using a bundled AI model (NSFWJS Inception v3). Because analysis runs locally, the image is never sent to an external moderation service. If the image falls into a category you've blocked, submission is stopped and the visitor sees a message you configure.
This add-on is for site owners who collect user-submitted images and want an automated first layer of moderation. It works with existing Post Image fields, including those on multi-page and AJAX-rendered forms.
Key features
- Supports JPG, PNG, WebP, and GIF uploads
- Samples multiple frames from animated GIFs when the browser supports it
- Classifies images into Drawing, Hentai, Neutral, Porn, and Sexy categories
- Lets you choose which categories to block and set sensitivity levels
- Displays a custom message to visitors when an image is blocked
- Configurable from Forms → Settings → NSFW Blocker
- Works with AJAX-rendered and multi-page Gravity Forms
Good for
Written from Ujwol Bastakoti’s own listing. Version and dates come straight from the source and are checked weekly.
Does the image get sent to an external service for analysis?
No. Classification runs entirely in the visitor's browser using a bundled model, so the image is never sent to a third-party API.
Which image formats are supported?
JPG, PNG, WebP, and GIF files are all supported. Animated GIFs can have multiple frames sampled when the browser supports frame decoding.
Which Gravity Forms field does this work with?
It requires a Gravity Forms Post Image field. It works with Post Image fields on standard, AJAX-rendered, and multi-page forms.
Is client-side detection reliable enough on its own?
The developer notes that automated classification can produce false positives and false negatives, so this check is best used as one layer within a broader moderation workflow.