nsfw_detect 2.1.1

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

2.1.1 - 2026-05-05

• Refresh pub.dev presentation with a clearer privacy-first README, shorter quickstarts, use cases, setup guidance, FAQ, and neutral visual-asset suggestions.
• Add versioned doc/ guides for getting started, permissions, media prechecks, picker workflows, library scanning, camera scanning, configuration, models, privacy, limitations, and troubleshooting.
• Expand Dartdoc coverage for the main detector APIs, scan and camera configuration, result types, sessions, controller lifecycle, and core widgets.
• Improve package metadata with repository, issue tracker, documentation, platform declarations, and search-friendly topics.
• Add GitHub issue and pull request templates for bug reports, feature requests, documentation issues, and contribution review.
• Exclude local agent/editor files from the published archive via .pubignore.
• Fix the example README package name and include the iOS example lockfile entry for app_settings.

2.1.0 — 2026-05-05

v2.1.0 ships a live-camera detection pipeline alongside the existing photo-library scan, plus a reusable permissions UI widget and the missing example/android/ shell.

New — Live camera scan pipeline

• NsfwDetector.startCameraScan(CameraConfiguration?) / stopCameraScan() — single-session live detection. Returns a CameraScanSession whose results is a broadcast Stream<CameraFrameResult>. Concurrent sessions throw a StateError.
• CameraConfiguration — modelId, confidenceThreshold, mode (classification or detection), fps (1–30, default 2), resolution (low / medium / high), detectionConfidenceThreshold, iouThreshold, iosComputeUnits, androidDelegate. Same model surface as the photo-library API.
• CameraFrameResult — per-frame value type with frameTimestamp, sorted labels, optional detections (NudeNet boxes), tolerant fromMap parsing. topCategory / topConfidence / isNsfw mirror ScanResult.
• CameraPermissionDeniedException, CameraErrorException — surfaced as stream errors on CameraScanSession.results (not as null per-frame results).

iOS native — AVCaptureSession + CoreML

• ios/Classes/camera/CameraSessionTask.swift, CameraFrameProcessor.swift, CameraConfiguration.swift, permissions/CameraPermission.swift.
• AVCaptureSession running at configurable FPS, frames thrown into the existing MLEngine.classify(pixelBuffer:) and MLDetectorEngine.detect(pixelBuffer:) paths — no duplicate inference code. Detection mode reuses NudeNet with NMS.
• Camera-permission flow via AVCaptureDevice.authorizationStatus / requestAccess(for: .video).
• Lifecycle: start, stop, restart are graceful — no buffer leaks, CVBufferPool reuse.
• Events multiplexed onto the existing nsfw_detect_ios/scan_events EventChannel with type ∈ {cameraFrameResult, cameraPermissionDenied, cameraError} — no new channels.

Android native — CameraX + TFLite

• android/.../camera/CameraSessionTask.kt, CameraFrameAnalyzer.kt, FpsThrottle.kt, ImageProxyConverter.kt, CameraSessionConfig.kt, CameraPermission.kt, PluginLifecycleOwner.kt.
• CameraX ImageAnalysis at the configured FPS, ImageProxy → Bitmap → TFLite reusing the existing TFLiteEngine and detector path. ImageProxy.close() in finally across all five analyzer branches.
• DetectionAggregator extracted from ScanSessionTask.runDetectionScan so the same body-part aggregation runs identically for library and camera frames (snapshot-compared — wire shape byte-identical).
• CameraX 1.3.4 (the last 1.3.x line) keeps minSdkVersion 21 and stock compileOptions.

Flutter — NsfwCameraView

• New widget: native preview via UiKitView (iOS) / AndroidView (Android) + HUD stack on top.
  • iOS PlatformView factory nsfw_detect_ios/camera_preview attaches AVCaptureVideoPreviewLayer (resizeAspectFill).
  • Android PlatformView wraps a PreviewView (FILL_CENTER); pulls in androidx.camera:camera-view:1.3.4.
• New NsfwCameraHud widget — confidence bar + category label + NSFW badge. Reuses NsfwResultBadge via a private ScanResult adapter (no duplicated badge code).
• Detection mode draws bounding boxes via the existing NsfwDetectionOverlay (it was already Size-agnostic).
• Optional blur on NSFW — BackdropFilter + ImageFilter.blur wrapped in AnimatedSwitcher so the blur fades in/out instead of strobing on borderline frames. Configurable blurSigma and enableBlurOnNsfw.
• Lifecycle bound to initState / dispose.
• NsfwGalleryTheme extended additively with four camera-only fields: cameraBlurSigma, cameraBlurTintOpacity, cameraConfidenceBarHeight, cameraHudBackgroundOpacity. Existing NsfwGalleryTheme(...) callers keep working unchanged.

New — NsfwPermissionsView

• Reusable widget that surfaces every permission the plugin needs (photo library + camera) with status, an explainer, and a Request / Open Settings action button. Auto-refreshes when the app returns from system Settings (WidgetsBindingObserver).
• Plugin stays dependency-free — the "Open Settings" deep-link is delegated to the host app via the onOpenSettings callback. Example app pulls in app_settings: ^5.1.1 to wire it.
• Wired into the example app's SettingsScreen above NsfwSettingsPanel.
• Themed via NsfwTheme semantic tokens (success / accent / danger), Semantics-wrapped for screen readers.
• New types in lib/src/api/permissions/permission_kind.dart: PermissionKind (photoLibrary, camera), PermissionStatus (authorized, limited, denied, permanentlyDenied, restricted, notDetermined), PhotoLibraryPermissionStatusMapping extension.

Platform-interface additions

• NsfwPlatformInterface.startCameraScan(CameraConfiguration) / stopCameraScan() — abstract; every platform must implement. Wired in NsfwMethodChannel.
• NsfwPlatformInterface.checkCameraPermission() / requestCameraPermission() — non-abstract with UnimplementedError defaults so test mocks need only stub what they exercise. NsfwMethodChannel re-maps MissingPluginException → UnimplementedError; NsfwDetector.checkCameraPermission() / requestCameraPermission() catch and degrade gracefully to PermissionStatus.notDetermined on platforms without the native handler yet wired.

Fixes

• Concurrent-task crash in ImageAnalyzer (iOS). pixelBufferPool was a lazy var; concurrent first-frame Tasks racing on the lazy initialisation produced a partially-written pointer, manifesting as EXC_BAD_ACCESS at +0x18 inside CVPixelBufferPool::createPixelBuffer on cooperative-queue threads. Eager-init in init() instead — pool depends only on inputSize, no behaviour or perf change.
• scanAsset returns result test regression. Pre-existing failure on main from before v2.1.0: the mock fixture mixed safe=0.95 with nudity=0.03, but ScanResult.topCategory has been NSFW-priority-sorted since v2.0 (commit 882ba2a). Reduced the fixture to a single unambiguous label and added an explicit test that locks in the priority-sort contract.

Example app

• example/ios/Runner/Info.plist — adds NSCameraUsageDescription. iOS killed the app on AVCaptureDevice.requestAccess without it.
• example/android/app/src/main/AndroidManifest.xml — adds android.permission.CAMERA + two optional <uses-feature> entries (camera + camera.autofocus with required="false") so the Play Store doesn't filter the listing on cameraless devices.
• example/lib/screens/settings_screen.dart embeds NsfwPermissionsView; depends on app_settings: ^5.1.1.

Tests

• 86 → 116 plugin unit + widget tests (+30). Camera HUD widget tests (Phase 4), NsfwPermissionsView tests covering all six PermissionStatus variants + lifecycle resume + UnimplementedError fallback, and the explicit NSFW-priority sort test.

Migration

// New: live camera scan
final session = await NsfwDetector.instance.startCameraScan(
  const CameraConfiguration(fps: 2, mode: ScanMode.detection),
);
session.results.listen((CameraFrameResult r) {
  if (r.isNsfw) print('NSFW @ ${r.frameTimestamp}: ${r.topCategory}');
});
// Or drop in the widget — it manages the session itself:
NsfwCameraView(
  config: const CameraConfiguration(fps: 2),
  enableBlurOnNsfw: true,
  showHudOverlay: true,
);

// New: permissions widget
NsfwPermissionsView(
  theme: appNsfwTheme,
  onOpenSettings: AppSettings.openAppSettings, // host-app dep
);

iOS callers must add to Info.plist:

<key>NSCameraUsageDescription</key>
<string>Live NSFW detection on the camera feed.</string>

Android callers must add to AndroidManifest.xml:

<uses-permission android:name="android.permission.CAMERA"/>

---

2.0.1

• Doc cleanup. No code changes from 2.0.0.

---

2.0.0

Breaking changes

• PickedMedia.mediaType is now MediaType enum (was String). pickedMedia.mediaType == 'image' → pickedMedia.mediaType == MediaType.image. Affects callers of NsfwDetector.instance.pickMedia(...).

Architecture — NsfwGalleryView is no longer a god-widget

• NsfwScanController extracts gallery state into a ChangeNotifier. Permission, scan session, item list, results map, progress and lifecycle methods (startScan, stopScan, requestPermission, dispose) live on the controller. Consumers can hold their own controller and bind multiple views to it, or rely on NsfwGalleryView to create+dispose one internally (default, BC).
• NsfwGalleryView.controller — new optional parameter. When null, the widget builds its own controller exactly like before. Otherwise the consumer owns the lifecycle.
• NsfwPlatformInterface slimmer for mocks. Optional methods (preloadModel, downloadModel, deleteModel, setModelUrl, setLogging, clearScanCache, resetScan, pickMedia, scanFilePath, scanImageBytes) ship default implementations — no-op or UnimplementedError. Test mocks now stub only the six lifecycle methods that actually matter.

New API

• NsfwScanController — see above. Exported from package:nsfw_detect/nsfw_detect.dart.
• MediaPickerType lives in lib/src/api/media_picker_type.dart (was inlined). Re-exported.

Internals & tests

• Test count 78 → 87 (+9): controller lifecycle, controller-vs-view interaction, PickedMedia.mediaType enum parsing, MediaPickerType standalone import.
• NsfwGalleryView shrunk from ~520 lines (logic + view) to a thin scaffold. Body is grid/skeleton/empty/permission render — driven by the controller.

Migration

// PickedMedia.mediaType
- if (pm.mediaType == 'image') { ... }
+ if (pm.mediaType == MediaType.image) { ... }

// Optional: hold your own controller
final controller = NsfwScanController(initialConfig: ScanConfiguration());
NsfwGalleryView(controller: controller, ...);
// remember to controller.dispose() in your State.dispose()

---

1.3.0

Object detection — body-part bounding boxes

• NudeNet detector (iOS + Android). New detection mode runs a YOLOv8-based 18-class body-part detector and emits bounding boxes per asset (e.g. FEMALE_BREAST_EXPOSED, FEMALE_GENITALIA_COVERED, …). Hosted as a downloadable model — ~46 MB .mlmodelc.zip (CoreML, with NMS pipeline producing VNRecognizedObjectObservation) / .tflite.zip (TFLite raw YOLO; Kotlin runs class-aware NMS at IoU 0.45). inputSize 640 for accuracy.
• MLDetectorEngine protocol (iOS + Android). First-class peer to MLEngine so the registry can hold both classifier and detector kinds. kind(for: id) distinguishes the two; detectorEngine(for:) / engine(for:) route to the correct factory.
• Detection-mode scan pipeline. When ScanConfiguration.mode == "detection" (or the chosen modelId is registered as a detector), ScanSessionTask routes pixel buffers through detect(...) and aggregates per-category max confidence into result.labels so the existing topCategory / isNsfw semantics keep working — extra raw boxes are stashed on result.detections.
• NSFW priority sort. Detection aggregation now sorts categories by NSFW priority (explicitNudity → nudity → suggestive → safe → unknown) before confidence. A high-confidence FACE_FEMALE no longer outranks a moderate-confidence FEMALE_BREAST_EXPOSED — any *_EXPOSED hit at or above the detection threshold flips the result to NSFW.
• NsfwDetectionOverlay widget. Drop-in overlay that renders detection boxes + labels + confidence on top of any image tile. Categories colour-coded via the existing theme tokens.
• ScanCache schema v2. New detections_json column persists raw bounding boxes per cached entry; cache hits replay both labels and detections. Migrations are wrapped in transactions; downgrade drops + recreates.

Models

• models-v1 GitHub Release consolidates all on-device artefacts as a single source of truth (Falconsai, AdamCodd, OpenNSFW2, NudeNetDetector). Defaults in both registries point there.
• Falconsai + AdamCodd Android. TFLite parity. ViT normalisation (2x − 1) and softmax baked into the graph so Kotlin passes raw [0, 1] floats and reads [0, 1] probabilities directly — symmetric to iOS via classifier_config. INT8 weight-only post-quantisation gets the artefacts to ~75 MB without measurable accuracy loss.
• Reproducible conversion. New tools/convert_models.py (CoreML), tools/convert_tflite.py (TFLite, runs in a separate venv to avoid the LLVM CLI clash between tensorflow and jax), and tools/convert_nudenet.py (ultralytics YOLO export → both formats). Each script is idempotent and gates download-then-convert from the upstream HuggingFace / GitHub source.

Fixes

• PHPhotosError 3303 fallback (iOS). When PHCachingImageManager refuses an asset (limited library, iCloud-only without network, cache edge cases), the analyzer retries via PHImageManager.default() + requestImageDataAndOrientation. Recovers most assets that previously surfaced as opaque "pixelBuffer unavailable".
• Detector preload (iOS). preloadModel and the implicit preload inside startScan both went through the classifier-only engine(for:) factory map and 404'd every detector model. Routes now branch on kind(for: id).
• Softmax for ViT classifiers (iOS). Falconsai / AdamCodd CoreML models emit raw logits via classifier_config. Vision passes those through as confidence, producing values like nudity=357%, safe=−240%. CoreMLEngine now applies a numerically-stable softmax client-side, so confidences land back in [0, 1] and the gallery's default 0..1 filter shows the NSFW hits.
• Surfaced video skip reasons. "Skipped" status now carries the underlying cause (zero-duration AVAsset, empty sample times, etc.) in result.errorMessage instead of disappearing silently.
• Surfaced pixel-buffer errors. Error from the per-asset image fetch is captured per-asset and propagated to result.errorMessage instead of being swallowed by try?.

Performance — large library scans

• Incremental scans (iOS + Android). Per-asset cache keyed by (localId, modelId, modificationDate) persisted in SQLite. A second sync of an unchanged 200k-asset library skips the ML pipeline entirely — sub-second filter pass instead of minutes of inference.
• Cached results are replayed by default. Stream<ScanResult> stays complete; cached items are flagged via the new ScanResult.fromCache: bool so consumers can distinguish them.
• Event-sink throttling (iOS). Per-asset result events are coalesced into batched results channel messages (50 items / 100 ms), and progress events are throttled to ≤ 1 per 100 ms. Reduces IPC roundtrips by ~50–100× for large libraries.
• Sliding-window prefetch (iOS). PHCachingImageManager now releases older prefetch windows via stopCachingImages(for:), keeping memory bounded regardless of library size (previously linear).
• Real cache hits in the ImageAnalyzer pipeline (iOS). Prefetched assets are now read through the same PHCachingImageManager with identical options — the prefetch was previously a no-op.
• Throttled checkpoint writes (iOS). Per-asset UserDefaults writes are coalesced (every 25 assets + at scan boundaries). Cuts disk I/O in the hot path proportionally.
• Batched scan-cache writes (iOS + Android). ScanCache.record() now buffers in memory and flushes in groups of 50 inside a single BEGIN…COMMIT transaction (iOS uses one prepared statement reused via bind/step/reset; Android uses SQLiteStatement + beginTransaction). 200k assets go from ~200k mini-transactions to ~4k. Reads call flush() first to keep lookups consistent.
• inSampleSize downsampling on bitmap decode (Android). BitmapFactory.decodeStream(...) previously did a full-resolution decode — a 12 MP photo allocated ~50 MB before being scaled to 224×224. Now a two-pass decode (inJustDecodeBounds + power-of-two inSampleSize) drops peak per-frame allocation by ~250×. Pendant to the iOS ImageIO thumbnail path.
• Pooled CVPixelBuffer + cached CGColorSpace (iOS). ImageAnalyzer now renders frames into buffers from a CVPixelBufferPool instead of CVPixelBufferCreate per frame, and the device RGB color space is allocated once statically. ~400k allocations eliminated on a 200k-asset scan.

Performance — inference acceleration

• Configurable Core ML compute units (iOS). New ScanConfiguration.iosComputeUnits exposes MLModelConfiguration.computeUnits. Defaults to .all; cpuAndNeuralEngine or cpuOnly can be faster on older devices without a dedicated ANE (no GPU roundtrip).

• TFLite GPU / NNAPI delegate (Android). New ScanConfiguration.androidDelegate enables the litert-gpu or NNAPI delegate. Opt-in (default CPU) because GPU/NNAPI delegates are flaky on some device families. Falls back to CPU silently if the delegate cannot be loaded.

• Reused VNCoreMLRequest (iOS). CoreMLEngine.classify(pixelBuffer:) no longer allocates a Vision request per call. One request is built at load() time and reused under a serializing lock — only relevant when the batch path falls back, but cleaner and cheaper.

• OSAllocatedUnfairLock for the scan counter (iOS 16+). Hot-path counter swaps NSLock for OSAllocatedUnfairLock<Int>. 5–10× cheaper under contention.

New API

• ScanConfiguration.skipAlreadyScanned: bool (default true) — skip assets matching a cached fingerprint.
• ScanConfiguration.forceRescan: bool (default false) — bypass the cache for this run; cache is overwritten on completion.
• ScanConfiguration.replayCachedResults: bool (default true) — emit cached results on hit; disable for "delta only" mode.
• ScanConfiguration.iosComputeUnits: IosComputeUnits (default .all) — selects the Core ML compute-unit preference on iOS.
• ScanConfiguration.androidDelegate: AndroidDelegate? (default null = CPU) — opt into the TFLite gpu or nnapi delegate on Android.
• NsfwDetector.instance.pickMedia({type, multiple, maxItems}) — opens the native picker and returns the selected items as List<PickedMedia> without classifying. Pair with scanAsset for on-demand classification.
• NsfwDetector.instance.clearScanCache({String? modelId}) — drop cached entries for one model or all.
• ScanResult.fromCache: bool — true when the result was replayed from the persistent cache.

Internals

• Schema migrations for the scan cache via PRAGMA user_version (iOS) / SQLiteOpenHelper.onUpgrade (Android), wrapped in transactions; onDowngrade drops + recreates rather than failing.
• New EventBatcher and CheckpointWriter helpers in ScanSessionTask.swift.
• MLEngine protocol gains setPreferredComputeUnits(_:) / loadedComputeUnits; ModelRegistry.engine(for:computeUnits:) recreates the cached engine if the preference changes.

Demo app

• New "Skip Already Scanned" and "Force Rescan" toggles in the settings screen.
• New "Clear Scan Cache" maintenance action.

---

1.2.8

Fixes

• Fixed ModelIds.adamcodd runtime classification mapping on iOS (AdamCodd output classes now map correctly to plugin categories).
• Fixed model-size handling for AdamCodd (ViT-384) in video frame sampling and file/bytes scan preprocessing.

---

1.2.7

Performance

• Improved runtime threshold consistency across native processing paths.
• Further stability refinements in background task flow under high load.

Known issue

• ModelIds.adamcodd is currently not functional at runtime. Use ModelIds.openNsfw2 or ModelIds.falconsai.

---

1.2.6

Performance

• Improved native runtime scheduling stability during long scans.
• Reduced unnecessary cancellation side effects in background task orchestration.

---

1.2.5

Fixes

• Fixed example app compile issues by removing obsolete detection-only UI code from the detail screen.
• Added a visible maintenance action in example settings to trigger NsfwDetector.resetScan().

---

1.2.4

Performance

• iOS method-channel heavy operations now run with utility-priority tasks to avoid accidental UI-thread contention during preload, model download, scanFile, scanBytes, and scanSingleAsset.
• Android background dispatch switched from per-asset raw thread spawning to a bounded native worker pool with backpressure, reducing thread churn and frame-drop risk under large scans.

---

1.2.3

New features

• Added NsfwDetector.resetScan() to reset native scan runtime state.
• Implemented native handling on iOS and Android (resetScan method-channel call).

---

1.2.2

Maintenance

• Native logging cleanup in platform internals.
• No API changes.

---

1.2.1

Maintenance

• Updated package metadata/topics for pub.dev discovery (nsfw-detection, nudity-detection).
• Version sync/release housekeeping (pubspec.yaml + iOS podspec).
• Minor runtime/performance cleanup: reduced logging overhead in native processing paths.
• No API changes.

---

1.2.0

New features

• NsfwDetector.pickAndScan({int maxItems}) — opens the native iOS PHPickerViewController (or Android photo picker on API 33+) and scans the selected photos/videos. Returns a ScanSession that streams results and progress exactly like startScan. No photo library permission required — the user picks items directly.
• NsfwDetector.scanFile(String filePath) — classifies a single image/video from a file path. Useful for scanning files from the app sandbox, document picker, etc.
• NsfwDetector.scanBytes(Uint8List bytes) — classifies a single image supplied as raw bytes (Uint8List). Useful when the image is already in memory (camera capture, network download, clipboard, etc.).
• Both scanFile and scanBytes accept an optional modelId and confidenceThreshold and return a ScanResult directly (no session required).

---

1.1.5

Breaking changes

• Removed BodyPartDetection, BoundingBox, NsfwSeverity, DetectionSummary — these types were never backed by a running model and always returned empty/null results.
• Removed ScanResult.detections, hasDetections, isDetectorResult, detectionSummary.

---

1.1.4

Docs

• Removed body part detection section from README and CHANGELOG — feature was never implemented.

---

1.1.3

Fixes

• CoreMLEngine.classifyBatch: removed invalid override keyword (protocol conformance, not subclass override)
• PixelBufferFeatureProvider: changed from struct to final class NSObject — MLFeatureProvider is an ObjC protocol requiring a class
• MLModel.predictions(from:): added required options: MLPredictionOptions() parameter (iOS 16+ SDK)
• performBackgroundGalleryScan: replaced internal ModelIds.falconsai default with string literal (public API constraint)

---

1.1.2

Improvements

• Auto model download in startScan — startScan now automatically downloads and compiles the model if it is not yet on disk. Calling downloadModel + preloadModel from Dart before startScan is no longer needed (they still work for manual preloading). All three steps run on native background threads; startScan returns to Dart immediately.
• NsfwDetectIosPlugin.performBackgroundGalleryScan — new public static method for running a gallery scan from a BGProcessingTask or similar context where no Flutter engine is present. Events are silently discarded; the completion callback is called when the scan finishes.

---

1.1.1

Fixes

• Fixed podspec version (was still 1.0.0) and removed placeholder author/homepage fields.
• Removed placeholder repository URL from pubspec.yaml.

---

1.1.0

Performance

• CoreML batch inference — images are now submitted to the model in batches via MLModel.predictions(from:), reducing ANE/GPU setup overhead from N times to once per batch. Typical throughput improvement: 1.5–3× faster on large photo libraries. Videos and Live Photos are unaffected; their pipeline is unchanged.
• Batch size is derived from the existing concurrency parameter — no Dart API change needed.
• Automatic fallback: if batch prediction fails twice in a row, the engine transparently reverts to the previous per-image Vision path for the remainder of the session.
• Added ScanConfiguration.disableBatchPrediction (default false) — set to true to force the serial Vision path, e.g. for diagnosing device-specific behaviour.

---

1.0.0

Initial release.

Core API

• NsfwDetector.instance singleton for all plugin operations
• startScan(ScanConfiguration) → ScanSession with streaming results, progress, and completion
• ScanSession.results — Stream<ScanResult> emitting results as each asset is classified
• ScanSession.progress — Stream<ScanProgress> with scanned/total counts and fraction
• ScanSession.done — Future<ScanSummary> resolving on completion or cancellation
• ScanSession.cancel() — graceful abort at any point
• scanAsset(localIdentifier) — single-asset synchronous-style scan
• requestPermission() / checkPermission() — photo library permission handling
• ScanConfiguration — immutable config: threshold, concurrency, video settings, model selection

Classification

• Five categories: safe, suggestive, nudity, explicitNudity, unknown
• ScanResult.isNsfw, topCategory, topConfidence, confidenceFor(category)
• ScanResult.labels — all categories sorted by confidence

Models

• OpenNSFW2 (CoreML, bundled, ~11 MB) — no download needed
• Falconsai NSFW and AdamCodd NSFW — downloadable via downloadModel()
• availableModels(), preloadModel(), downloadModel(), deleteModel(), setModelUrl()

Widgets

• NsfwGalleryView — drop-in gallery with live scan, result badges, and custom tile/thumbnail builders
• NsfwResultBadge — 4 badge styles: compact, detailed, iconOnly, minimal
• NsfwScanProgressBar — 3 display styles: linear, compact, textOnly
• NsfwGalleryTheme — full color + style customization with copyWith
• NsfwScanControls — start/stop control bar
• NsfwMediaTile — composable tile widget for custom layouts

Platform support

• iOS 16+ — CoreML + Vision + Apple Neural Engine acceleration
• Android API 24+ — TensorFlow Lite inference, tiered permission handling (API 21 → 33+)

Video scanning

• Uniform temporal frame sampling
• Hard-threshold fast-exit (score > 0.9 → immediate flag)
• Center-weighted aggregation to reduce title-card false positives
• Configurable maxVideoFrames and videoFrameInterval