See Witness in action
Try one of our examples to see how a Witness forensic report will look — multi-layer analysis from L0 verdict to L4 verifiable claims. Results shown are illustrative.
Try an example
Drop a file here or click to upload
Image (JPG, PNG) or video (MP4, MOV) up to 50 MB
Media in, forensic report out
Every scan runs an orchestration pipeline of independent detection methods. The output is a standardized forensic report with five layers of analysis.
Summary
Single verdict with confidence score and one-paragraph explanation.
Overlays
Attention heatmaps and artifact density maps highlighting suspicious regions.
Attribution
Likely generation method and model family identification with forensic fingerprints.
Narrative
AI-generated plain-language explanation of findings, with hallucination caveat.
Verifiable claims
Juror-checkable factual claims with measurable thresholds and evidence.
How Witness analyzes content
Every scan runs multiple independent detection methods in parallel, then combines their signals into a single verdict with a transparent breakdown.
Diffusion fingerprinting
Detects noise residual patterns left by AI image generators like Stable Diffusion, Midjourney, DALL-E, and Flux.
Generative artifact scan
Identifies spectral artifacts and frequency anomalies from GANs, autoencoders, and neural network upsampling.
Pixel-level analysis
Examines neighboring pixel relationships and upsampling traces invisible to the human eye.
Temporal consistency
Analyzes frame-to-frame motion, facial dynamics, and temporal coherence across video clips.
Audio & lip-sync
Checks voice authenticity and phoneme-level lip-audio alignment to detect voice clones and lip-sync manipulation.
Provenance & metadata
Reads C2PA content credentials and EXIF metadata. A valid cryptographic signature beats any probabilistic score.
Images, video, and audio
Images
Diffusion fingerprinting, generative artifact analysis, pixel-level forensics, face integrity checks, content provenance, and metadata analysis.
Video
AI video generation detection, face-deepfake analysis, temporal consistency, lip-sync verification, audio voice analysis, and per-frame scanning.
Audio
Voice clone detection, spectral analysis, phoneme-level speech verification, transcript coherence, and micro-prosody analysis.
Integrate detection everywhere
Witness is the verification layer for the AI era. A simple API call or MCP tool returns a real-or-AI verdict that your agents, pipelines, and applications can act on.
curl -X POST https://api.witness.vision/v1/detect \
-H "Authorization: Bearer wt_live_..." \
-F "file=@suspect-image.jpg"
{
"verdict": "synthetic",
"confidence": 0.94,
"modality": "image",
"report": {
"l0_summary": "Synthetic — 94% confidence",
"l2_attribution": "Latent diffusion model",
"l4_claims": [
"Diffusion noise residual detected (KL: 0.85)",
"No EXIF camera metadata present",
"NPR score 0.91 (natural range: 0.00–0.25)"
]
},
"report_pdf": "https://api.witness.vision/reports/..."
}// AI agents call Witness as an MCP tool
// Claude, GPT, or any MCP-compatible agent
> "Check if this image is AI-generated"
Tool call: witness.detect({
url: "https://example.com/photo.jpg"
})
→ {
verdict: "real",
confidence: 0.96,
summary: "Authentic — camera sensor noise
profile confirmed, EXIF metadata present,
no synthetic generation indicators.",
report_url: "https://..."
}REST API
Single endpoint. Send any image, video, or audio file. Get a structured verdict with confidence score and full forensic report.
MCP Server
Give AI agents the ability to verify content. Any MCP-compatible model can call Witness as a tool and act on the result.
Python & JS SDKs
Native client libraries with type-safe responses, automatic retries, and streaming support for batch processing.
Webhooks
Asynchronous processing for large files. Submit media, get a webhook callback with the full L0–L4 forensic report when analysis completes.
Built for integration
Flag AI-generated uploads before they reach your platform.
Verify source media authenticity before publication.
Generate forensic reports with verifiable claims for evidence review.
Detect manipulated documents and identity photos in submission pipelines.
Let autonomous agents verify content they encounter via MCP tool calls.
Label or flag synthetic content at upload time with real-time API checks.