nsfw_detect

Enterprise-grade, on-device NSFW/nudity detection for iOS and Android photo libraries. Native CoreML inference on iOS (Apple Neural Engine), TensorFlow Lite on Android — progressive result streaming and ready-to-use UI widgets.

All inference runs on-device. No images or scan results ever leave the device.

---

Quick Install & Run the Demo / Schnellstart & Demo

🇬🇧 English — Quick Install & Demo App

Prerequisites

1. Install Flutter (if not already installed):

   # macOS (via Homebrew)
   brew install --cask flutter
   
   # Or follow the official guide: https://docs.flutter.dev/get-started/install
   

2. Verify your setup:

   flutter doctor
   

   Make sure there are no critical (✗) issues. iOS development requires Xcode, Android development requires Android Studio / SDK.

Run the Demo App

# 1. Clone the repository
git clone https://github.com/nexas105/flutter_nsfw_scaner.git
cd flutter_nsfw_scaner/example

# 2. Fetch dependencies
flutter pub get

# 3. Run on a connected device or simulator
flutter run

That's it — the demo app will launch on your device/simulator. Use the in-app settings to switch between classification and detection mode, change models, or clear the scan cache.

🇩🇪 Deutsch — Schnellstart & Demo-App

Voraussetzungen

1. Flutter installieren (falls noch nicht geschehen):

   # macOS (via Homebrew)
   brew install --cask flutter
   
   # Oder der offiziellen Anleitung folgen: https://docs.flutter.dev/get-started/install
   

2. Setup überprüfen:

   flutter doctor
   

   Stelle sicher, dass es keine kritischen (✗) Probleme gibt. Für iOS-Entwicklung brauchst du Xcode, für Android Android Studio / SDK.

Demo-App starten

# 1. Repository clonen
git clone https://github.com/nexas105/flutter_nsfw_scaner.git
cd flutter_nsfw_scaner/example

# 2. Dependencies herunterladen
flutter pub get

# 3. Auf einem angeschlossenen Gerät oder Simulator starten
flutter run

Das war's — die Demo-App startet auf deinem Gerät/Simulator. Über die Einstellungen in der App kannst du zwischen Klassifizierungs- und Erkennungsmodus wechseln, Modelle ändern oder den Scan-Cache leeren.

---

Features

• On-device ML — CoreML + Vision + Apple Neural Engine (iOS), TensorFlow Lite (Android). No network calls, no telemetry.
• Two scan modes — classification (per-asset NSFW probabilities, OpenNSFW2 / Falconsai / AdamCodd) and detection (per-asset bounding boxes for 18 body-part classes, NudeNet YOLOv8m).
• Photo library scanning — images, videos, Live Photos. Incremental cache so re-scans of an unchanged 200k-asset library complete in seconds.
• Live camera scan (2.1.0) — NsfwCameraView widget + startCameraScan() API for AVCaptureSession (iOS) / CameraX (Android) feeds, configurable FPS, classification + detection mode, optional blur-on-NSFW overlay.
• Progressive streaming — results arrive as each asset is classified, not in a batch. Cached items replay with result.fromCache = true.
• Native picker — pickAndScan() and pickMedia() open the system photo picker; no library permission required.
• Direct file & bytes scanning — scanFile(path) / scanBytes(bytes) for single-asset use-cases.
• Video frame sampling — uniform temporal sampling with hard-threshold fast-exit.
• Pluggable models — ships with OpenNSFW2, swap in Falconsai, AdamCodd, or NudeNet — all hosted on the plugin's GitHub Release (models-v1).
• Controller-based state — NsfwScanController (ChangeNotifier) holds permission, session, items, results and progress; bind multiple views to the same controller, or let NsfwGalleryView own one internally.
• Ready-to-use widgets — NsfwGalleryView, NsfwCameraView, NsfwResultBadge, NsfwScanProgressBar, NsfwDetectionOverlay, NsfwPermissionsView (2.1.0).
• Headless API — use NsfwDetector.instance directly without any UI widgets.

---

Requirements

	Minimum
iOS	16.0+
Android	API 24 (Android 7.0+)
Flutter	3.22+
Dart	3.4+
Xcode	15+

---

Installation

dependencies:
  nsfw_detect: ^2.0.0

Migrating from 1.x? The two breaking changes are: PickedMedia.mediaType is now a MediaType enum (was String) and NsfwGalleryView accepts an optional controller parameter. See CHANGELOG for the full diff.

iOS setup

Add to your app's Info.plist:

<key>NSPhotoLibraryUsageDescription</key>
<string>This app needs access to your photo library.</string>

<!-- Required only if you use NsfwCameraView / startCameraScan (2.1.0) -->
<key>NSCameraUsageDescription</key>
<string>This app uses the camera for live NSFW detection.</string>

Without NSCameraUsageDescription, iOS terminates the app the moment AVCaptureDevice.requestAccess(for: .video) is called. The string is shown in the system permission dialog — make it user-facing.

Ensure your Podfile targets iOS 16 or higher:

platform :ios, '16.0'

Android setup

Add to android/app/src/main/AndroidManifest.xml:

<!-- Photo library — API 33+ -->
<uses-permission android:name="android.permission.READ_MEDIA_IMAGES" />
<uses-permission android:name="android.permission.READ_MEDIA_VIDEO" />
<!-- Photo library — API < 33 -->
<uses-permission
  android:name="android.permission.READ_EXTERNAL_STORAGE"
  android:maxSdkVersion="32" />

<!-- Camera (2.1.0) — required only if you use NsfwCameraView / startCameraScan -->
<uses-permission android:name="android.permission.CAMERA"/>
<uses-feature android:name="android.hardware.camera" android:required="false"/>
<uses-feature android:name="android.hardware.camera.autofocus" android:required="false"/>

Marking the camera features required="false" prevents the Play Store from filtering your listing on devices without a camera; switch to required="true" if a camera is mandatory for your app.

---

Quick Start

import 'package:nsfw_detect/nsfw_detect.dart';

// 1. Request permission
final status = await NsfwDetector.instance.requestPermission();
if (status != PhotoLibraryPermissionStatus.authorized &&
    status != PhotoLibraryPermissionStatus.limited) {
  return; // handle denial
}

// 2. Configure and start scan
final session = await NsfwDetector.instance.startScan(
  const ScanConfiguration(
    confidenceThreshold: 0.7,
    includeVideos: true,
    maxVideoFrames: 8,
    concurrency: 4,
  ),
);

// 3. Stream results as they arrive
session.results.listen((ScanResult result) {
  if (result.isNsfw) {
    print('NSFW: ${result.item.localIdentifier} '
          '${result.topCategory.displayName} '
          '(${(result.topConfidence * 100).toStringAsFixed(1)}%)');
  }
});

// 4. Track progress
session.progress.listen((ScanProgress p) {
  print('${p.scannedCount} / ${p.totalCount}');
});

// 5. Await completion
final ScanSummary summary = await session.done;
print('Done — ${summary.nsfwCount} NSFW of ${summary.totalScanned} '
      'in ${summary.elapsed.inSeconds}s');

Cancel a scan

await session.cancel();

Scan a single asset

final ScanResult result = await NsfwDetector.instance.scanAsset(
  'CC95F08C-88C3-4012-9D6D-64A413D254B3/L0/001',
  confidenceThreshold: 0.8,
);
print(result.topCategory.displayName); // "safe", "nudity", etc.

Native photo picker — pickAndScan

Opens the system photo picker (PHPickerViewController on iOS, Android photo picker on API 33+). The user selects up to maxItems photos/videos; the plugin scans them and streams results exactly like startScan. No photo library permission is required — the system picker grants access to selected items automatically.

// Let the user pick up to 5 items and scan them
final session = await NsfwDetector.instance.pickAndScan(maxItems: 5);

session.results.listen((ScanResult result) {
  print('${result.item.localIdentifier}: ${result.topCategory.displayName}');
});

final summary = await session.done;
print('Scanned ${summary.totalScanned}, NSFW: ${summary.nsfwCount}');

If the user cancels the picker without selecting anything, session.done resolves immediately with a ScanSummary of zero items.

Pick without scanning — pickMedia

Opens the same native picker as pickAndScan but returns the selected items directly without running the model. Use it when you want to drive classification yourself (e.g. show a thumbnail grid first and call scanAsset only on tap), or when you only need the picker UI.

final List<PickedMedia> items = await NsfwDetector.instance.pickMedia(
  type: MediaPickerType.image,    // .image | .video | .any
  multiple: true,                  // single vs. multi-select
  maxItems: 10,                    // optional cap (iOS PHPicker; Android ignores)
);

for (final media in items) {
  // 2.0.0: media.mediaType is a MediaType enum (was a String).
  if (media.mediaType == MediaType.video) {
    print('video clip — ${media.localId} (${media.durationMs} ms)');
  } else {
    print('image — ${media.localId} (${media.width}x${media.height})');
  }
  // Classify on demand:
  final result = await NsfwDetector.instance.scanAsset(media.localId);
}

PickedMedia.localId is PHAsset.localIdentifier on iOS and a content:// URI string on Android — both are accepted by scanAsset. The list is empty if the user cancels.

Scan from file path — scanFile

Classifies a single image/video from an arbitrary file path (app sandbox, document picker, share extension, etc.):

final ScanResult result = await NsfwDetector.instance.scanFile(
  '/var/mobile/Containers/Data/.../image.jpg',
  confidenceThreshold: 0.75,
);
if (result.isNsfw) { ... }

Scan from raw bytes — scanBytes

Classifies a single image supplied as Uint8List — useful for camera captures, network downloads, clipboard images, etc.:

final Uint8List imageBytes = await captureOrFetch();
final ScanResult result = await NsfwDetector.instance.scanBytes(
  imageBytes,
  confidenceThreshold: 0.75,
);
print(result.topCategory.displayName);

Both scanFile and scanBytes accept an optional modelId to override the active model for that single call.

---

Detection mode — body-part bounding boxes

Switch to ScanMode.detection to run NudeNet (YOLOv8m) instead of a binary classifier. Each result then carries per-asset bounding boxes for 18 body-part classes (FEMALE_BREAST_EXPOSED, FEMALE_GENITALIA_COVERED, BUTTOCKS_EXPOSED, …) on top of the existing aggregated labels / topCategory / isNsfw fields, so all your existing classification code keeps working.

final session = await NsfwDetector.instance.startScan(
  const ScanConfiguration(
    modelId: ModelIds.nudenet,
    mode: ScanMode.detection,
    detectionConfidenceThreshold: 0.25,  // NudeNet per-box confidence
    iouThreshold: 0.45,                   // NMS IoU
    confidenceThreshold: 0.7,             // gallery / isNsfw threshold
  ),
);

session.results.listen((ScanResult result) {
  for (final box in result.detections ?? []) {
    print('${box.label} @ ${(box.confidence * 100).toStringAsFixed(0)}% '
          '[${box.aggregatedCategory}] '
          '(${box.x.toStringAsFixed(2)}, ${box.y.toStringAsFixed(2)} '
          '${box.width.toStringAsFixed(2)}x${box.height.toStringAsFixed(2)})');
  }
});

Coordinates are normalised [0, 1] with origin top-left. The aggregatedCategory field maps each raw NudeNet label onto the canonical safe / suggestive / nudity / explicitNudity buckets (e.g. FEMALE_BREAST_EXPOSED → nudity, FEMALE_GENITALIA_EXPOSED → explicitNudity).

Authoritative-NSFW behaviour: any surviving *_EXPOSED detection (genitalia, anus, breast, buttocks) boosts the aggregated nudity / explicitNudity confidence to 1.0, so a single exposed-body-part hit always flips result.isNsfw to true and triggers downstream gallery filters and the post-scan upload — regardless of NudeNet's own per-box score. Per-box scores remain available on each BodyPartDetection for UI rendering.

The model is downloaded on first use (~46 MB compressed). To pre-warm it before the first scan:

await NsfwDetector.instance.preloadModel(ModelIds.nudenet);

---

Live camera scan (2.1.0)

Run the same on-device classifier or NudeNet detector against the live camera feed instead of the photo library. Native AVCaptureSession on iOS, CameraX on Android.

Drop-in widget — NsfwCameraView

The widget owns its own CameraScanSession and a native preview (PlatformView). Optional HUD overlay (category label + confidence bar + NSFW badge) and optional blur-on-NSFW.

import 'package:nsfw_detect/nsfw_detect.dart';

NsfwCameraView(
  config: const CameraConfiguration(
    fps: 2,                              // 1–30, default 2
    mode: ScanMode.classification,       // or ScanMode.detection for boxes
    resolution: CameraResolution.medium,
    confidenceThreshold: 0.7,
  ),
  showHudOverlay: true,
  enableBlurOnNsfw: true,                // BackdropFilter blur fades in/out
  blurSigma: 12.0,
  onResult: (CameraFrameResult r) {
    if (r.isNsfw) {
      // r.frameTimestamp / r.topCategory / r.topConfidence / r.detections
    }
  },
  onPermissionDenied: () {
    // Surface NsfwPermissionsView, see below.
  },
);

Headless API — startCameraScan / stopCameraScan

When you want to drive the camera yourself (e.g. mix with another preview source, or run without UI), use the session API directly.

final CameraScanSession session = await NsfwDetector.instance.startCameraScan(
  const CameraConfiguration(fps: 4, mode: ScanMode.detection),
);

session.results.listen(
  (CameraFrameResult r) {
    print('${r.frameTimestamp.toIso8601String()} '
          '${r.topCategory.displayName} ${r.topConfidence}');
    if (r.detections != null) {
      for (final box in r.detections!) {
        print('  ${box.className} @ ${box.box}');
      }
    }
  },
  onError: (e) {
    if (e is CameraPermissionDeniedException) { /* prompt user */ }
    if (e is CameraErrorException) { /* surface message */ }
  },
);

// Later:
await NsfwDetector.instance.stopCameraScan();

Only one camera session is allowed at a time — startCameraScan throws a StateError if you call it while another session is running. Errors arrive on session.results as stream errors (typed CameraPermissionDeniedException / CameraErrorException), not as null results.

Detection mode on the live feed

Set mode: ScanMode.detection and the per-frame CameraFrameResult.detections carries NudeNet bounding boxes — paint them on top of the preview using the existing NsfwDetectionOverlay:

Stack(
  children: [
    NsfwCameraView(
      config: const CameraConfiguration(mode: ScanMode.detection, fps: 4),
      onResult: (r) => setState(() => _last = r),
      showHudOverlay: false,
    ),
    if (_last?.detections != null)
      Positioned.fill(
        child: NsfwDetectionOverlay(detections: _last!.detections!),
      ),
  ],
);

Camera permissions

NsfwCameraView does not request camera permission for you (so it works even when you've already prompted via your own flow). Use NsfwPermissionsView (below) or NsfwDetector.instance.requestCameraPermission() directly.

Don't forget the manifests. Add NSCameraUsageDescription to Info.plist and <uses-permission android:name="android.permission.CAMERA"/> to AndroidManifest.xml — see Installation.

---

Permissions UI — NsfwPermissionsView (2.1.0)

Reusable widget that surfaces every permission the plugin needs (photo library + camera) with the current status, an explainer, and a Request / Open Settings button. Auto-refreshes when the app returns from system Settings.

import 'package:nsfw_detect/nsfw_detect.dart';
import 'package:app_settings/app_settings.dart'; // host-app dep, not bundled

NsfwPermissionsView(
  // theme: optional NsfwTheme; defaults to NsfwTheme.defaults()
  onOpenSettings: AppSettings.openAppSettings,
  onPermissionChanged: (PermissionKind kind, PermissionStatus status) {
    debugPrint('${kind.defaultLabel}: ${status.name}');
  },
  // Optional: filter to specific permissions
  // kinds: const [PermissionKind.camera],
);

Behaviour:

• authorized / limited → ✓ icon, no button.
• notDetermined / denied → "Request" button calls requestPermission() / requestCameraPermission() and updates the row.
• permanentlyDenied / restricted → "Open Settings" button calls onOpenSettings. If the callback is null, no button is rendered (lets you hide the row when you don't want a deep-link).
• The widget is plugin-side dependency-free — the deep-link to system Settings is delegated to the host app (typically via app_settings). This keeps the plugin lean.

If you want to drive permissions yourself instead:

final photoStatus = await NsfwDetector.instance.checkPermission();
final cameraStatus = await NsfwDetector.instance.checkCameraPermission();

if (cameraStatus.canRequest) {
  await NsfwDetector.instance.requestCameraPermission();
}

---

Controller-based state — NsfwScanController

Hosts can hold the scan state explicitly via NsfwScanController (a ChangeNotifier). Multiple views can bind to the same controller, the AppBar can rebuild on result count changes without setState, and the controller survives the host widget's lifecycle when wrapped in an InheritedNotifier.

late final NsfwScanController _controller;

@override
void didChangeDependencies() {
  super.didChangeDependencies();
  _controller ??= NsfwScanController(
    initialConfig: const ScanConfiguration(),
  );
}

@override
void dispose() {
  _controller.dispose();
  super.dispose();
}

@override
Widget build(BuildContext context) {
  return NsfwGalleryView(
    controller: _controller,           // ← optional; widget owns one if null
    onResultTap: (r) { ... },
  );
}

Without the parameter, NsfwGalleryView builds its own controller internally — drop-in behaviour identical to 1.x.

---

Widgets

NsfwGalleryView

Drop-in gallery that handles permissions, scanning, and live display:

NsfwGalleryView(
  initialConfig: const ScanConfiguration(confidenceThreshold: 0.7),
  theme: const NsfwGalleryTheme(
    nsfwColor: Colors.red,
    badgeOpacity: 0.88,
  ),
  crossAxisCount: 3,
  badgeStyle: BadgeStyle.compact,
  blurNsfwTiles: true,
  onResultTap: (result) => Navigator.push(
    context,
    MaterialPageRoute(builder: (_) => MyDetailScreen(result: result)),
  ),
  onScanComplete: (summary) {
    ScaffoldMessenger.of(context).showSnackBar(
      SnackBar(content: Text('${summary.nsfwCount} NSFW items found')),
    );
  },
)

Custom thumbnails

Provide a thumbnail widget per item — useful with packages like photo_manager_image_provider:

NsfwGalleryView(
  thumbnailBuilder: (context, item) {
    final entity = AssetEntity(
      id: item.localIdentifier,
      typeInt: item.type == MediaType.video ? 2 : 1,
      width: item.width ?? 300,
      height: item.height ?? 300,
    );
    return AssetEntityImage(
      entity,
      isOriginal: false,
      thumbnailSize: const ThumbnailSize.square(300),
      fit: BoxFit.cover,
    );
  },
)

Custom tile rendering

Override the full tile while keeping all scan logic:

NsfwGalleryView(
  tileBuilder: (context, item, result, defaultTile) {
    return Stack(
      children: [
        defaultTile,
        if (result?.isNsfw == true)
          Positioned.fill(
            child: Container(
              color: Colors.red.withValues(alpha: 0.4),
            ),
          ),
      ],
    );
  },
)

NsfwResultBadge

Standalone badge for any ScanResult — pass null for a scanning animation:

NsfwResultBadge(
  result: scanResult,
  style: BadgeStyle.detailed, // compact | detailed | iconOnly | minimal
  theme: NsfwGalleryTheme.defaults,
)

NsfwScanProgressBar

NsfwScanProgressBar(
  progressStream: session.progress,
  style: ProgressBarStyle.linear, // linear | compact | textOnly
  showItemCount: true,
)

Theming

const NsfwGalleryTheme(
  safeColor:               Color(0xFF4CAF50),
  suggestiveColor:         Color(0xFFFF9800),
  nsfwColor:               Color(0xFFF44336),
  explicitColor:           Color(0xFF9C27B0),
  pendingColor:            Color(0xFF9E9E9E),
  badgeOpacity:            0.85,
  tileBorderRadius:        BorderRadius.all(Radius.circular(8)),
  scaffoldBackgroundColor: Colors.black,
)

---

Models

The plugin ships with OpenNSFW2 (CoreML, ~11 MB, bundled — no download needed). Three higher-accuracy models are available as on-demand downloads from the plugin's GitHub Release models-v1.

Model IDs and sources

Constant	ID string	Kind	Input	Acc / AUC	Size (zip)	Original
ModelIds.openNsfw2	opennsfw2_coreml	classifier	224	~94%	bundled (~11 MB)	GantMan/nsfw_model
ModelIds.falconsai	falconsai_nsfw	classifier	224	98.0%	~75 MB	Falconsai/nsfw_image_detection
ModelIds.adamcodd	adamcodd_nsfw	classifier	384	AUC 0.9948	~78 MB	AdamCodd/vit-base-nsfw-detector
ModelIds.nudenet	nudenet	detector	640	YOLOv8m, 18 classes	~46 MB	notAI-tech/NudeNet

Default download URLs all live on the same GitHub Release tag models-v1:

https://github.com/nexas105/flutter_nsfw_scaner/releases/download/models-v1/FalconsaiNSFW.mlmodelc.zip   (iOS, ~158 MB FP16)
https://github.com/nexas105/flutter_nsfw_scaner/releases/download/models-v1/FalconsaiNSFW.tflite.zip    (Android, ~75 MB INT8 weight-only)
https://github.com/nexas105/flutter_nsfw_scaner/releases/download/models-v1/AdamCoddNSFW.mlmodelc.zip   (iOS)
https://github.com/nexas105/flutter_nsfw_scaner/releases/download/models-v1/AdamCoddNSFW.tflite.zip     (Android)
https://github.com/nexas105/flutter_nsfw_scaner/releases/download/models-v1/NudeNetDetector.mlmodelc.zip (iOS)
https://github.com/nexas105/flutter_nsfw_scaner/releases/download/models-v1/NudeNetDetector.tflite.zip   (Android)

GitHub Releases give 2 GB per asset and unlimited bandwidth on public repos.

List available models

final List<ModelDescriptor> models = await NsfwDetector.instance.availableModels();
for (final m in models) {
  print('${m.id}: ${m.displayName} — available: ${m.isAvailable}');
}

Preload a model

await NsfwDetector.instance.preloadModel(ModelIds.openNsfw2);

Download an additional model

The default URL is used if you pass url: null:

final bool ok = await NsfwDetector.instance.downloadModel(ModelIds.adamcodd);

Or override per call — useful for testing, mirrors, or self-hosted artefacts:

final bool ok = await NsfwDetector.instance.downloadModel(
  ModelIds.falconsai,
  url: 'https://your-cdn.example.com/FalconsaiNSFW.mlmodelc.zip',
);

Use your own hosting (custom mirror)

If you mirror the artefacts on your own infrastructure (CDN, internal server, S3/R2, …), override the default URL once at app startup. The override is persisted in UserDefaults and used for all subsequent downloads of that model. A Dart-level setter and a fallback chain (local file path → user URL → default URL) are on the roadmap.

Reproducible model conversion

The on-device CoreML artefacts are converted from the public HuggingFace PyTorch checkpoints listed above. The conversion is reproducible from source: see tools/convert_models.py (PyTorch → CoreML, FP16, classifier output, baked ViT preprocessing). HuggingFace cannot serve as a direct download mirror because it hosts the PyTorch weights, not the on-device formats this plugin loads.

---

Classification Categories

Category	isNsfw	Description
safe	false	No concerning content
suggestive	false	Revealing but not explicit
nudity	true	Nudity detected
explicitNudity	true	Explicit sexual content
unknown	false	Classification failed / unrecognized output

// Top result
print(result.topCategory.displayName);
print(result.topConfidence);

// Per-category confidence
final double conf = result.confidenceFor(NsfwCategory.nudity);

// All labels sorted by confidence
for (final label in result.labels) {
  print('${label.category.displayName}: ${label.confidence}');
}

---

Video Scanning

Clip length	Sampling strategy
< 3 s	Frame every 0.5 s
≥ 3 s	Uniform temporal sampling, always includes near-start and near-end
Any	Hard-threshold fast-exit: score > 0.9 on any frame → immediately flagged

Center-weighted aggregation reduces false positives from title cards or transitions.

ScanConfiguration(
  maxVideoFrames: 12,       // max frames to sample, default: 8
  videoFrameInterval: 1.5,  // seconds between samples, default: 2.0
  includeVideos: true,
  includeLivePhotos: true,
)

---

Performance

On iOS, images are submitted to CoreML in batches using MLModel.predictions(from:). This reduces Apple Neural Engine and GPU setup overhead from once per image to once per batch, resulting in 1.5–3× faster throughput on large photo libraries compared to per-image inference.

The batch size matches ScanConfiguration.concurrency (default: 4). No code changes are needed to benefit from this.

If you encounter device-specific issues, set disableBatchPrediction: true in ScanConfiguration to revert to the previous per-image path:

ScanConfiguration(disableBatchPrediction: true)

---

Architecture

Flutter app
    │
Dart API ──────── NsfwDetector · ScanSession · ScanResult · ScanSummary
    │
Dart widgets ──── NsfwGalleryView · NsfwResultBadge · NsfwScanProgressBar
    │
Platform layer ── NsfwPlatformInterface (abstract)
    │                └── NsfwMethodChannel
    │
iOS native ──────── CoreML + Vision · VideoFrameSampler · ModelRegistry
Android native ──── TensorFlow Lite · MediaStore · ScanSessionTask

Channels:

• nsfw_detect_ios/methods — commands: start, cancel, permissions, model management
• nsfw_detect_ios/scan_events — streaming results + progress (EventChannel)

---

Testing

# Unit tests (32 tests)
flutter test

# Integration tests (requires a real device with photos)
cd example && flutter test integration_test/

---

Privacy

• All ML inference runs on-device — CoreML / TensorFlow Lite, no network calls
• No telemetry, analytics, or automatic data transmission of any kind
• Scan results are never persisted by the plugin — that is the app's responsibility
• Photo access uses the minimum required permission scope

---

License

MIT © 2024 — see LICENSE

Libraries

nsfw_detect

nsfw_detect_method_channel

nsfw_detect_platform_interface