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Why is this AI?
Submit any link for multi-layer AI diagnostics: metadata inspection, AI hashtag extraction, community reviewer consensus, and automated claim fact-checking.
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Multi-Layer Diagnostics
How the AI Analysis Engine Works
Our platform combines metadata scraping, explicit AI signal scanning, reviewer consensus, and search-backed fact checking.
01
Link Scraping & Metadata
Scrapes target links across YouTube, TikTok, Instagram, Twitter/X, and Facebook. Extracts OpenGraph metadata, page titles, author tags, and thumbnail structures.
02
AI Keyword & Hashtag Scan
Scans title, description, and tags for explicit AI indicators including #AIart, #midjourney, #dalle, #stableDiffusion, ChatGPT tags, and generator metadata.
03
Forensic Texture & Lighting Audit
Examines visual anomalies including synthetic smoothness, shadow direction mismatches, physical movement flaws, and audio spectral distortion.
04
Reviewer Consensus Scoring
Collects independent votes and structured observations from community reviewers to score content probability and flag specific flaws.
05
Automated Claim Fact-Checking
Extracts verifiable statements from video captions or post descriptions and cross-references them against web sources to detect hallucination or misinformation.
06
Structured Diagnostic Report
Synthesizes observations into a clear report featuring verdict direction (AI-Generated, Human-Created, or Unclear), confidence rating, and an anomalies checklist.
Deep Analysis Breakdown
What We Audit on Every Submission
Every submitted link undergoes automated structural checks alongside human reviewer evaluation.
Metadata & OpenGraph Inspection
We extract embedded page parameters to identify original upload tags, missing camera EXIF data, platform source signatures, and preview metadata.
og:title
og:description
Source Platform
Upload Signatures
AI Hashtag & Keyword Detection
Automated regex pattern matching identifies prompt terms, model keywords, and generator tags embedded directly in creator descriptions or titles.
#AIart
Midjourney
DALL-E
Stable Diffusion
AI Generated
Claim Verification & Fact Checking
Factual claims within the text or video transcript are evaluated for accurate dates, real-world events, and source consistency to detect AI hallucinations.
Fact Extraction
Date Verification
Source Cross-Ref
Hallucination Flag
Forensic & Consensus Diagnostics
Evaluates visual texture coherence, audio synchronization, robotic phrasing patterns, and lighting physics with consensus agreement from community reviewers.
Texture Noise
Lighting Physics
Audio Rhythm
Anomalies List
Deep Verification Reports
Deep Verification Reports in Action
Explore real analysis reports generated by our diagnostic pipeline, combining metadata extraction, claim checking, and consensus scoring.
Platform Highlights
Why WeCatchAI Diagnostics Are Superior
Built for users who demand clear explanations rather than black-box AI scores.
Deep Media Diagnostics
Examines texture anomalies, lighting inconsistencies, fluid dynamics, and audio spectral patterns for synthetic signatures.
Community Consensus
Submissions pass through balanced reviewer voting to ensure detection reflects genuine human observation rather than false positive filters.
Full Transparency
Verifiable authenticity reports including consensus data, confidence ratings, claim fact checks, and specific anomaly descriptions.
Recent Submissions
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