nsfw_detect

Privacy-friendly NSFW detection for Flutter apps, running fully on-device.

Use nsfw_detect to scan images, videos, selected media, photo libraries, and camera frames locally with Core ML on iOS and TensorFlow Lite on Android.

Detection is probabilistic. Use it as a local moderation signal and one layer in a broader safety workflow.

Features

On-device NSFW detection for Flutter apps
Image, video, photo library, native picker, file, bytes, and camera scanning
iOS support via Core ML and Vision
Android support via TensorFlow Lite
Stream-based scan results and progress updates
Configurable confidence thresholds and model selection
Classification categories: safe, suggestive, nudity, explicit nudity, unknown
Optional detection mode with body-part bounding boxes
Incremental scan cache for large media libraries
Ready-to-use widgets and headless Dart APIs
No telemetry or automatic media transmission by the plugin

Installation

dependencies:
  nsfw_detect: ^2.1.1

Then run:

flutter pub get

Platform requirements

Platform	Minimum
iOS	16.0+
Android	API 24 / Android 7.0+
Flutter	3.22+
Dart	3.4+
Xcode	15+

Quickstart

import 'package:flutter/foundation.dart';
import 'package:nsfw_detect/nsfw_detect.dart';

final status = await NsfwDetector.instance.requestPermission();

if (status != PhotoLibraryPermissionStatus.authorized &&
    status != PhotoLibraryPermissionStatus.limited) {
  // Show your permission UI or fallback flow.
  return;
}

final session = await NsfwDetector.instance.startScan(
  const ScanConfiguration(
    confidenceThreshold: 0.75,
    includeVideos: true,
    maxVideoFrames: 8,
  ),
);

session.results.listen((result) {
  if (result.isNsfw) {
    debugPrint(
      '${result.item.localIdentifier}: '
      '${result.topCategory.displayName} '
      '${(result.topConfidence * 100).toStringAsFixed(1)}%',
    );
  }
});

session.progress.listen((progress) {
  debugPrint('${progress.scannedCount}/${progress.totalCount}');
});

final summary = await session.done;
debugPrint('Scanned ${summary.totalScanned} items.');

For media review flows, selected media, library scans, camera scans, configuration, and model handling, see the guides below.

Documentation

API reference is available on pub.dev.

Example app

Run the example app from the repository:

git clone https://github.com/nexas105/flutter_nsfw_scaner.git
cd flutter_nsfw_scaner/example
flutter pub get
flutter run

A real iOS or Android device is recommended for photo library and camera workflows. The example app demonstrates gallery scanning, headless API usage, result detail screens, model selection, confidence thresholds, and video frame options.

Privacy

nsfw_detect is designed for local-first media analysis.

Inference runs on-device
Inference runs in the native iOS and Android layers
The plugin does not include analytics or telemetry
Picker-based scanning can avoid full photo-library permission

Your app is still responsible for explaining permissions, handling results, storing any moderation state, and complying with platform, privacy, and safety requirements.

Limitations

NSFW detection is probabilistic. Results can include false positives and false negatives, especially with unusual lighting, partial visibility, illustrations, screenshots, low-resolution media, compressed videos, or ambiguous content.

Tune confidence thresholds for your product risk. For sensitive workflows, combine on-device detection with user reporting, human review, policy-specific rules, or other moderation layers.

Package links

License

MIT. See LICENSE.