nsfw_detect 2.6.1

Privacy-friendly NSFW detection for Flutter apps. Analyze images, videos, picked media, photo libraries, and camera frames on-device.

nsfw_detect

Privacy-friendly NSFW detection for Flutter apps. On-device, no telemetry, no media uploads.

import 'package:nsfw_detect/nsfw_detect.dart';

// Works for images, videos, GIFs — same call, same result shape.
final result = await NsfwDetector.instance.scanFile('/path/to/file.jpg');
if (result.isNsfw) {
  // Blur, block, or route to review — your choice.
}

That's the whole API for the most common case. No init, no permission for files on disk. Add more entry points as you need them.

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

---

Install

dependencies:
  nsfw_detect: ^2.6.0

flutter pub get

Platform	Minimum
iOS	16.0+
Android	API 24 / Android 7.0+
Web	one-shot APIs only — see below
Flutter	3.22+
Dart	3.4+
Xcode	15+

---

Web

The web platform runs the one-shot scan APIs in the browser — scanBytes, scanFile (a blob:/http(s): URL), pickMedia, and detection-mode scans. Inference runs client-side: classification on nsfwjs (TensorFlow.js), detection on NudeNet via onnxruntime-web. The JS runtimes load on demand from a CDN — no index.html edits required.

// Detection-mode scans need a NudeNet model — point this at a
// CORS-reachable .onnx URL once, before the first scan.
NsfwWebConfig.nudeNetModelUrl = 'https://your-host.example/nudenet_320n.onnx';

final result = await NsfwDetector.instance.scanBytes(bytes);

Not available on web: photo-library scanning (startScan), camera scanning, and background sweep — they have no browser equivalent and throw UnimplementedError. nsfwjs has no dedicated nudity class, so the web classifier reports explicitNudity rather than nudity, and its confidence scores are not numerically comparable to the native OpenNSFW2 classifier.

---

What you can scan

Source	API	Permission
Image file on disk	scanFile · isNsfwFile	none
Video file on disk (mp4, mov, …)	scanFile · isNsfwFile	none
Animated image (gif, apng, webp)	scanFile	none
Bytes in memory	scanBytes · isNsfwBytes	none
Flutter ImageProvider	scanImageProvider	none
Remote URL (image or video)	scanUrl	none (network)
Photo-library asset (image or video)	scanAsset · isNsfwAsset	photo library
System picker (image or video)	pickMedia · pickAndScan	none (per-item access)
Whole library (photos + videos)	startScan	photo library
Live camera	startCameraScan	camera
Mixed batch	scanPaths(['file://…', 'https://…', '/abs/path', 'asset-id'])	per-source

Videos are first-class. scanFile auto-detects the container and samples frames at a configurable interval. No separate API or model is needed.

Each headless API returns a ScanResult (full label list + detections) or a shortcut Future<bool> via the isNsfw* variants.

---

Common patterns

Gate an image before display

NsfwModerationGate.file(
  '/path/to/upload.jpg',
  child: Image.file(File('/path/to/upload.jpg')),
)

Constructors: .bytes(...), .file(...), .asset(...). Optional confidenceFloor adds a manual-review band; pass nsfwBuilder / uncertainBuilder / errorBuilder for custom UI.

Pick + scan in one call

final session = await NsfwDetector.instance.pickAndScan(maxItems: 5);
await for (final r in session.results) {
  if (r.isNsfw) { /* … */ }
}

pickMedia (returns the picked items without scanning) is the other half of that API.

Scan a URL before showing it

final r = await NsfwDetector.instance.scanUrl(
  Uri.parse('https://cdn.example.com/avatar.jpg'),
  timeout: const Duration(seconds: 8),
);
if (r.isNsfw) /* hide / report */

Hard-capped at 32 MB by default to keep a malicious server from OOM-ing you. Override via maxBytes.

Find perceptual duplicates

final clusters = await NsfwDetector.instance.findDuplicates(
  items, // List<MediaItem>
  loadBytes: (id) async => await myStorage.read(id),
);
// clusters: List<List<MediaItem>> — each cluster ≥ 2 visually-identical items.

dHash + LRU cache; the detector decouples from your storage layer via loadBytes.

Redact detector boxes in place

final redacted = await NsfwDetector.instance.redactBytes(
  bytes,
  result,
  mode: RedactionMode.blur, // or .pixelate, .blackBox
  intensity: 0.8,
);

When result.detections is non-empty, only the per-detection boxes are redacted. Falls back to whole-image redaction for classifier-only results.

Scan a video file

final result = await NsfwDetector.instance.scanFile('/path/to/clip.mp4');
if (result.isNsfw) {
  // result.topCategory, result.topConfidence — same shape as image scans.
}

The same API works for .mov, .gif, .apng, and .webp. The plugin samples frames automatically and aggregates them into one ScanResult. Control the sampling with ScanConfiguration:

final result = await NsfwDetector.instance.scanFile(
  '/path/to/clip.mp4',
  configuration: const ScanConfiguration(
    maxVideoFrames: 12,       // default 8 — more frames, more accurate
    videoFrameInterval: 1.0,  // default 2.0 s — sample every second
  ),
);

Whole-library scan with progress

final session = await NsfwDetector.instance.requestPermissionAndStartScan(
  // includeVideos: true is the default — shown explicitly for clarity.
  const ScanConfiguration.strict(includeVideos: true),
);
if (session == null) return; // User denied — show your permission UI.

session.results.listen((r) { if (r.isNsfw) /* … */ });
session.progress.listen((p) => print('${p.scannedCount}/${p.totalCount}'));
final summary = await session.done;

Presets: .strict() (threshold 0.85), .moderate() (0.7), .permissive() (0.5), .fastScan() (concurrency 8). Pass includeVideos: false to skip video assets and scan images only.

Pre-warm models on splash

await NsfwDetector.instance.init(const NsfwInitOptions(
  preloadModels: [ModelIds.openNsfw2],
  enableNativeLogging: false,
));

Skipping init is fine — the plugin lazy-loads on first use. Use NsfwInitOptions.lazy() / .debug() / .production() for typical shapes.

Drop-in permissions UI

NsfwPermissionsView(
  kinds: const [PermissionKind.photoLibrary, PermissionKind.camera],
  onOpenSettings: () => /* host opens system Settings */,
)

The plugin doesn't pull in permission_handler or app_settings; pass onOpenSettings to wire your preferred deep-link package.

Per-category thresholds

final config = ScanConfiguration.moderate().copyWith(
  thresholdsByCategory: {
    NsfwCategory.explicitNudity: 0.5,  // flag aggressively
    NsfwCategory.suggestive: 0.95,     // tolerate
  },
);

Overrides the scalar confidenceThreshold per category; unmapped categories fall back to it. ScanResult.withThresholds(...) re-evaluates a persisted result without re-running inference.

Remember moderator decisions

NsfwDetector.instance.useDecisionStore(SharedPreferencesDecisionStore());
await NsfwDetector.instance.decisions.mark('asset-id', ScanDecision.allow);
// Later scans of that asset come back with `userDecision` applied —
// .allow forces isNsfw=false, .block forces isNsfw=true.

InMemoryDecisionStore is the dependency-free default; SharedPreferencesDecisionStore persists across cold starts.

Detect, then classify each region

final r = await NsfwDetector.instance.scanFileDetectThenClassify(
  '/path/to/image.jpg',
  detectorModelId: ModelDescriptor.nudenet,
);
// r.detections[i].labels — per-region NSFW classification, stronger than
// detector-only (graded confidence) or classifier-only (per-region attribution).

Telemetry hooks

NsfwDetector.instance.onTelemetryEvent = (e) => myAnalytics.log(e);

Structured scanCompleted / modelLoaded / downloadFinished / … events with timing and a PII-free confidence decile. localId only attaches when includeLocalIdsInTelemetry is set. The plugin itself sends nothing — this is a local callback.

Localize plugin strings

NsfwLocalizations.current = const NsfwLocalizationsDe();

Bundled EN/DE/ES/FR/JA cover category names, permission hints, confidence buckets, and widget button labels. NsfwLocalizations.resolve('es-MX') picks a bundle by BCP-47 tag.

---

What's new

2.5.x — platform reach + polish

• Localization — NsfwLocalizations plain-Dart bundle (EN/DE/ES/FR/JA), no new deps. Global override via NsfwLocalizations.current.
• Accessibility — Semantics pass over the surfaced widgets; WCAG-AA badge contrast via NsfwGalleryTheme.readableForeground.

2.4.0 — architectural moves

• Detect-then-classify pipeline — ScanMode.detectThenClassify, scanBytesDetectThenClassify / scanFileDetectThenClassify; per-region labels on BodyPartDetection.labels.
• Per-category thresholds — ScanConfiguration.thresholdsByCategory, ScanResult.withThresholds.
• Persistent decision store — DecisionStore (InMemory* / SharedPreferences*), NsfwDetector.decisions, ScanResult.userDecision.
• Telemetry hooks — NsfwDetector.onTelemetryEvent, PII-free by default.
• Evaluation harness — tools/eval/ precision / recall / F1 reporting + a false-positive regression suite.

2.3.0 — headless inputs + redaction

• scanUrl, scanImageProvider, scanPaths (auto-routing batch); findDuplicates + PerceptualHash JSON.
• Native redaction — redactBytes / redactFile with RedactionMode.blur / .pixelate / .blackBox.
• prefetchAssets, cachedResult + cacheUpdates, NsfwSafetyProfile.evaluate.
• Background sweep scheduling, multi-model ensemble voting, runtime custom model registration.

Full list in CHANGELOG.md.

---

Result shape

class ScanResult {
  final MediaItem item;
  final ScanStatus status;       // completed | failed | skipped
  final DateTime scannedAt;
  final List<NsfwLabel> labels;  // sorted: NSFW labels first, then by confidence
  final List<BodyPartDetection> detections; // detector-mode only
  final ScanDecision? userDecision;          // from the DecisionStore, if any
  // … convenience getters: isNsfw, topCategory, topConfidence,
  //   hasNudity, hasExplicitContent, isSuggestive, hasDetections,
  //   confidenceDescription
}

Category	isNsfw	Typical handling
safe	false	allow
suggestive	false	optional warning
nudity	true	block or blur
explicitNudity	true	block / route to review
unknown	false	apply your fallback policy

result.isNsfw is true only when the scan completed AND the top category is NSFW AND confidence ≥ the threshold.

ScanResult.toJson() / fromJson(...) round-trip preserves the threshold so isNsfw is stable across persistence.

---

Models

Id	Shape	Size	Source
ModelIds.openNsfw2	classifier, 224	~11 MB	downloaded on first use
ModelIds.falconsai	classifier, 224 (ViT)	~75 MB	opt-in download
ModelIds.adamcodd	classifier, 384 (ViT)	~75 MB	opt-in download
ModelDescriptor.nudenet	detector, 640 (YOLOv8m body-parts)	~46 MB	opt-in download

Set a custom mirror URL with setModelUrl(modelId, url). The model archive's SHA-256 is verified before extraction when pinned on the descriptor. Manage downloads / preloads via NsfwDetector.instance.models (NsfwModelManager).

---

Permissions

Workflow	iOS	Android
scanFile · scanBytes · scanUrl · scanImageProvider	none	none
pickMedia · pickAndScan	none (picker grants per item)	none
scanAsset · startScan	NSPhotoLibraryUsageDescription	READ_MEDIA_IMAGES + READ_MEDIA_VIDEO (API 33+) / READ_EXTERNAL_STORAGE (≤32)
startCameraScan	NSCameraUsageDescription	CAMERA

The plugin requests at runtime via requestPermission / requestCameraPermission. NsfwPermissionsView is a drop-in panel showing live status with a Request button.

---

Documentation

• Getting started
• Cookbook — common patterns
• Permissions
• Media precheck
• Picker workflows
• Library scanning
• Camera scanning
• Configuration
• Models
• Platform gotchas (iOS / Android)
• Performance tuning
• False positives FAQ
• Privacy and limitations
• Troubleshooting

API reference on pub.dev.

---

Example app

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

A real device is recommended for photo-library and camera workflows — the iOS simulator has no camera, and emulator photo libraries are usually empty. The example covers the gallery view, picker flow, camera scanner, result detail, moderation gate, and model selection.

---

Privacy

• Inference runs on-device on Core ML (iOS) and TFLite (Android). The plugin sends no analytics and performs no telemetry network egress.
• onTelemetryEvent is a local callback — it hands scan events to your code; nothing leaves the device unless you forward it.
• Picker-based scanning avoids full photo-library permission — the system picker grants per-item access.
• scanUrl is the only Dart-initiated network egress the plugin performs; everything else is local. Model downloads are explicit calls or the auto-download path the host opts into via NsfwInitOptions.downloadIfMissing.

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

---

Limitations

NSFW detection is probabilistic. Expect false positives and false negatives on unusual lighting, partial visibility, illustrations, screenshots, low-resolution media, compressed video, or ambiguous content.

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

---

Links

• pub.dev package
• API documentation
• GitHub repository
• Issue tracker
• Changelog

License

MIT. See LICENSE.