{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_watermarkly","slug":"watermarkly","name":"Watermarkly","type":"product","url":"https://watermarkly.com","page_url":"https://unfragile.ai/watermarkly","categories":["image-generation"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_watermarkly__cap_0","uri":"capability://image.visual.ai.powered.face.detection.and.blur.application","name":"ai-powered face detection and blur application","description":"Automatically detects human faces in images using deep learning computer vision models (likely MTCNN, RetinaFace, or similar face detection architectures) and applies configurable blur filters to detected regions without manual selection. The system processes image tensors through a pre-trained neural network to identify face bounding boxes, then applies Gaussian or pixelation blur kernels to those regions in real-time or batch mode.","intents":["I need to redact faces from 50+ screenshots before sharing them with my team","I want to automatically blur faces in user-submitted photos without manually selecting each one","I need to ensure GDPR/HIPAA compliance by removing identifiable facial data from images at scale"],"best_for":["Content moderation teams processing high-volume user-generated content","Healthcare providers de-identifying patient photos for research or training","Remote work teams redacting faces from screenshots before external sharing"],"limitations":["Detection accuracy degrades with extreme angles, occlusions, or low-resolution faces (<50px)","Cannot distinguish between intentional subjects and background faces—may over-blur","No fine-grained control over blur strength per face or region without manual adjustment","Requires manual review for sensitive use cases (legal, medical) due to false negatives"],"requires":["Image file in JPEG, PNG, or WebP format","Minimum 640x480 resolution recommended for reliable detection","Internet connection for cloud-based model inference (if not running locally)"],"input_types":["image (JPEG, PNG, WebP)"],"output_types":["image (JPEG, PNG, WebP with applied blur masks)"],"categories":["image-visual","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_watermarkly__cap_1","uri":"capability://image.visual.license.plate.and.text.detection.with.selective.blur","name":"license plate and text detection with selective blur","description":"Extends face detection to identify and blur sensitive text regions (license plates, ID numbers, addresses, email addresses) using optical character recognition (OCR) combined with object detection. The system likely uses CRAFT or similar text detection models to locate text bounding boxes, optionally runs OCR to classify sensitive patterns (regex matching for phone numbers, license plate formats), and applies blur only to flagged regions.","intents":["I need to redact license plates from parking lot photos before posting on social media","I want to automatically blur visible addresses and phone numbers from screenshots","I need to remove readable text from images containing sensitive information"],"best_for":["Legal teams redacting documents and evidence photos","Real estate agents removing property addresses from listing photos","Insurance adjusters de-identifying accident scene photos"],"limitations":["OCR accuracy depends on text size, font, and image quality—small or stylized text often missed","Pattern matching (regex) for sensitive data requires pre-configured rules; custom patterns may not be supported","Cannot distinguish between sensitive and non-sensitive text without manual review (e.g., blurs all numbers, not just SSNs)","Rotated or perspective-distorted text detection is unreliable"],"requires":["Image file in JPEG, PNG, or WebP format","Minimum 300 DPI or equivalent pixel density for reliable text detection","Internet connection for cloud-based OCR/detection models"],"input_types":["image (JPEG, PNG, WebP)"],"output_types":["image (JPEG, PNG, WebP with text regions blurred)"],"categories":["image-visual","safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_watermarkly__cap_2","uri":"capability://automation.workflow.batch.image.processing.with.parallel.inference","name":"batch image processing with parallel inference","description":"Processes multiple images sequentially or in parallel through the detection and blur pipeline, likely using a job queue system (Redis, RabbitMQ, or similar) to distribute inference workloads across GPU/CPU resources. The system accepts a folder or file list, queues detection jobs, applies blur to each image, and returns a batch of processed images with progress tracking and error handling for failed detections.","intents":["I need to process 200 screenshots in one operation without clicking 'blur' 200 times","I want to set up a recurring workflow that automatically blurs new images uploaded to a folder","I need to process images in bulk while my team continues other work"],"best_for":["Content moderation teams handling high-volume daily submissions","Legal discovery teams processing hundreds of documents with embedded images","Healthcare organizations de-identifying patient photo archives"],"limitations":["Processing time scales linearly with batch size unless GPU acceleration is available","No built-in scheduling or automation—requires manual batch upload or external integration","Failed detections on individual images don't halt the batch, but may require manual review","Storage and bandwidth costs scale with batch size; unclear if Watermarkly charges per-image or per-batch"],"requires":["Batch size typically limited to 100-1000 images per operation (product-dependent)","Internet connection for cloud processing","File storage (local or cloud) to stage input/output images"],"input_types":["image (JPEG, PNG, WebP)","folder/directory path or file list"],"output_types":["image (JPEG, PNG, WebP with blur applied)","batch processing report (success/failure count)"],"categories":["automation-workflow","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_watermarkly__cap_3","uri":"capability://image.visual.customizable.blur.intensity.and.style.selection","name":"customizable blur intensity and style selection","description":"Provides user-configurable blur parameters (Gaussian blur radius, pixelation block size, motion blur direction) and style presets (light, medium, heavy redaction) that are applied uniformly or selectively to detected regions. The system likely stores blur configuration as metadata or presets, allowing users to adjust blur strength before or after detection without re-running the detection model.","intents":["I want to blur faces lightly so they're still somewhat visible, but unidentifiable","I need to completely obscure license plates with heavy pixelation","I want different blur strengths for different regions (light blur for background faces, heavy for foreground)"],"best_for":["Content creators balancing privacy with visual context (e.g., showing a scene without identifying individuals)","Legal teams applying consistent redaction standards across document batches","Accessibility-conscious teams who want to preserve image readability while protecting privacy"],"limitations":["No per-region customization without manual selection—all detected faces/text get the same blur strength","Limited blur style options (likely Gaussian, pixelation, and maybe motion blur—no custom kernels)","Blur strength presets may not match specific regulatory requirements (e.g., GDPR 'unidentifiable' thresholds)","No preview of blur effect before batch processing, requiring trial-and-error"],"requires":["UI access to blur settings (slider, dropdown, or preset selector)","No additional dependencies or API keys"],"input_types":["blur configuration (intensity: 1-10, style: 'gaussian'|'pixelate'|'motion')"],"output_types":["image (JPEG, PNG, WebP with configured blur applied)"],"categories":["image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_watermarkly__cap_4","uri":"capability://automation.workflow.web.based.ui.with.drag.and.drop.image.upload","name":"web-based ui with drag-and-drop image upload","description":"Provides a browser-based interface (likely React or Vue.js frontend) with drag-and-drop file upload, real-time preview of detected regions before blur application, and one-click download of processed images. The UI communicates with a backend API (REST or GraphQL) to submit images for processing and retrieve results, with progress indicators and error messages for failed detections.","intents":["I want to quickly blur a few images without installing software","I need to see what the AI detected before I apply blur","I want to download my blurred images immediately after processing"],"best_for":["Non-technical users who avoid command-line tools or desktop software","Teams using shared devices or cloud-only workflows","Freelancers and small businesses without IT infrastructure"],"limitations":["Web-based processing requires uploading images to Watermarkly's servers, raising privacy concerns for sensitive data","No offline mode—requires internet connection and trust in server-side data handling","File size limits typical for web uploads (likely 10-50MB per image)","Preview rendering may lag for large batches or high-resolution images","No integration with local file systems or cloud storage (Google Drive, Dropbox) mentioned"],"requires":["Modern web browser (Chrome, Firefox, Safari, Edge)","JavaScript enabled","Internet connection","No installation or API key required"],"input_types":["image file (drag-and-drop or file picker)","image URL (if supported)"],"output_types":["image file (download to local device)"],"categories":["automation-workflow","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_watermarkly__cap_5","uri":"capability://image.visual.detection.confidence.scoring.and.manual.override","name":"detection confidence scoring and manual override","description":"Returns confidence scores for each detected region (face, text, license plate) indicating the model's certainty, allowing users to filter or review low-confidence detections before applying blur. The system likely provides a review interface where users can accept/reject individual detections, adjust bounding boxes, or manually add missed regions before finalizing blur application.","intents":["I want to see which detections the AI is uncertain about before blurring","I need to manually add a face that the AI missed","I want to reject false positives (e.g., a face-like pattern in the background)"],"best_for":["Legal and healthcare teams requiring high-accuracy redaction with audit trails","Quality assurance teams validating AI detection before bulk processing","Users processing images with challenging conditions (low light, occlusion, unusual angles)"],"limitations":["Manual review defeats the purpose of automation—adds back the time savings for high-volume workflows","Confidence thresholds are model-dependent and may not correlate with actual accuracy","No built-in audit trail or version history of manual overrides","Bounding box adjustment UI may be clunky or require precise mouse control"],"requires":["Access to detection review interface (web UI or API)","Time for manual review (defeats automation for large batches)"],"input_types":["detection results (bounding boxes, confidence scores)"],"output_types":["validated detections (user-approved bounding boxes)"],"categories":["image-visual","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_watermarkly__cap_6","uri":"capability://image.visual.multi.format.image.export.with.quality.preservation","name":"multi-format image export with quality preservation","description":"Exports blurred images in multiple formats (JPEG, PNG, WebP) with configurable compression levels and quality settings, preserving metadata (EXIF, color profile) or stripping it for privacy. The system likely uses image encoding libraries (libvips, ImageMagick, or native browser APIs) to transcode the blurred image tensor into the selected format with user-specified quality parameters.","intents":["I need to export images as PNG for lossless archival","I want to reduce file size by exporting as WebP for web sharing","I need to strip EXIF metadata to remove location and camera information"],"best_for":["Teams with specific file format requirements (legal discovery, medical records)","Content creators optimizing for web performance","Privacy-conscious users removing metadata before sharing"],"limitations":["Blur application may introduce compression artifacts, especially with JPEG export","Quality settings are format-specific and may not be intuitive (JPEG quality 0-100 vs PNG compression 0-9)","No batch export format conversion—each image must be exported individually or in bulk with the same settings","Metadata stripping is all-or-nothing; no selective metadata removal"],"requires":["Export format selection (JPEG, PNG, WebP)","Quality/compression parameter (if applicable)"],"input_types":["blurred image (in-memory tensor or temporary file)"],"output_types":["image file (JPEG, PNG, WebP)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_watermarkly__cap_7","uri":"capability://automation.workflow.preset.templates.for.common.redaction.scenarios","name":"preset templates for common redaction scenarios","description":"Offers pre-configured redaction profiles (e.g., 'Legal Document', 'Healthcare Photo', 'Social Media Screenshot') that bundle detection sensitivity, blur strength, and export settings optimized for specific use cases. The system likely stores these as configuration templates that users can select before processing, with optional customization of individual parameters.","intents":["I need to redact a legal document photo—what settings should I use?","I want to follow healthcare privacy best practices without researching HIPAA requirements","I'm preparing screenshots for social media—what blur level is appropriate?"],"best_for":["Non-technical users unfamiliar with privacy regulations or redaction best practices","Teams enforcing consistent redaction standards across departments","Organizations with compliance requirements (HIPAA, GDPR, CCPA)"],"limitations":["Presets are generic and may not match specific organizational policies or regulatory requirements","No ability to create custom presets or save user-defined configurations","Presets may over-blur or under-blur depending on image content (e.g., 'Legal' preset assumes high-contrast documents)","No documentation of which regulations or standards each preset follows"],"requires":["Preset selection UI (dropdown or template gallery)","No additional dependencies"],"input_types":["preset name (e.g., 'Legal Document', 'Healthcare Photo')"],"output_types":["configuration object (detection sensitivity, blur strength, export format)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["Image file in JPEG, PNG, or WebP format","Minimum 640x480 resolution recommended for reliable detection","Internet connection for cloud-based model inference (if not running locally)","Minimum 300 DPI or equivalent pixel density for reliable text detection","Internet connection for cloud-based OCR/detection models","Batch size typically limited to 100-1000 images per operation (product-dependent)","Internet connection for cloud processing","File storage (local or cloud) to stage input/output images","UI access to blur settings (slider, dropdown, or preset selector)","No additional dependencies or API keys"],"failure_modes":["Detection accuracy degrades with extreme angles, occlusions, or low-resolution faces (<50px)","Cannot distinguish between intentional subjects and background faces—may over-blur","No fine-grained control over blur strength per face or region without manual adjustment","Requires manual review for sensitive use cases (legal, medical) due to false negatives","OCR accuracy depends on text size, font, and image quality—small or stylized text often missed","Pattern matching (regex) for sensitive data requires pre-configured rules; custom patterns may not be supported","Cannot distinguish between sensitive and non-sensitive text without manual review (e.g., blurs all numbers, not just SSNs)","Rotated or perspective-distorted text detection is unreliable","Processing time scales linearly with batch size unless GPU acceleration is available","No built-in scheduling or automation—requires manual batch upload or external integration","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.67,"ecosystem":0.2,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:34.117Z","last_scraped_at":"2026-04-05T13:23:42.559Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=watermarkly","compare_url":"https://unfragile.ai/compare?artifact=watermarkly"}},"signature":"lPPTyOIdibBxTEPRtk3Ol+CW/bBx4sTY188S/E3AIqe6RhOt8Yxvx+X0syH9IjIusZ5Eoi0QPDUhPUac5PMiDQ==","signedAt":"2026-06-21T18:21:17.188Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/watermarkly","artifact":"https://unfragile.ai/watermarkly","verify":"https://unfragile.ai/api/v1/verify?slug=watermarkly","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}