Embed & Detect

1. Embed a watermark

Send your content to /fingerprint/embed to extract the TKG and embed the watermark.

curl -X POST "https://api.factagora.com/api/v1/fingerprint/embed" \
  -H "Authorization: Bearer fa_your_api_key" \
  -H "Content-Type: application/json" \
  -d '{
    "content": "The European Central Bank raised interest rates by 25 basis points on Thursday, citing persistent core inflation in the eurozone. Analysts at Goldman Sachs expect one more hike before a pause in Q4.",
    "content_type": "news",
    "metadata": {
      "author_id": "editor_jane",
      "published_at": "2024-06-15T08:00:00Z",
      "source_id": "article_12345"
    }
  }'

import requests

resp = requests.post(
    "https://api.factagora.com/api/v1/fingerprint/embed",
    headers={"Authorization": "Bearer fa_your_api_key"},
    json={
        "content": "The European Central Bank raised interest rates by 25 basis points on Thursday, citing persistent core inflation in the eurozone. Analysts at Goldman Sachs expect one more hike before a pause in Q4.",
        "content_type": "news",
        "metadata": {
            "author_id": "editor_jane",
            "published_at": "2024-06-15T08:00:00Z",
            "source_id": "article_12345",
        },
    },
)
data = resp.json()

const resp = await fetch("https://api.factagora.com/api/v1/fingerprint/embed", {
  method: "POST",
  headers: {
    Authorization: "Bearer fa_your_api_key",
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    content:
      "The European Central Bank raised interest rates by 25 basis points on Thursday, citing persistent core inflation in the eurozone. Analysts at Goldman Sachs expect one more hike before a pause in Q4.",
    content_type: "news",
    metadata: {
      author_id: "editor_jane",
      published_at: "2024-06-15T08:00:00Z",
      source_id: "article_12345",
    },
  }),
});
const data = await resp.json();

Response

{
  "watermarked_content": "The European Central Bank raised interest rates by 25 basis points...",
  "fingerprint_id": "fp_l1p8OPCwGhvu",
  "tkg_snapshot": {
    "entities": ["European Central Bank", "Interest rate", "Goldman Sachs"],
    "timeseries": ["2024-06-15"],
    "relations": [
      { "from": "European Central Bank", "rel": "raises", "to": "Interest rate" }
    ],
    "argument_map": [
      {
        "premise": "Persistent core inflation in the eurozone",
        "evidence": "ECB policy meeting decision",
        "conclusion": "Interest rates raised by 25 basis points"
      }
    ]
  },
  "embed_timestamp": "2024-06-15T08:01:23.456Z",
  "meta": { "executionTimeMs": 1234 }
}

Distribute watermarked_content, not the original. The watermarked version looks identical to the original but contains invisible zero-width characters that enable watermark-based detection.

Store fingerprint_id alongside your internal article ID. You’ll need it for re-scoring and reporting.

2. Detect content reuse

When you encounter content that looks like it might originate from your article, even if it’s been rewritten, send it to /fingerprint/detect.

curl -X POST "https://api.factagora.com/api/v1/fingerprint/detect" \
  -H "Authorization: Bearer fa_your_api_key" \
  -H "Content-Type: application/json" \
  -d '{
    "content": "ECB hiked rates 25bp Thursday due to stubborn inflation. Goldman analysts predict one final increase before year-end pause."
  }'

resp = requests.post(
    "https://api.factagora.com/api/v1/fingerprint/detect",
    headers={"Authorization": "Bearer fa_your_api_key"},
    json={
        "content": "ECB hiked rates 25bp Thursday due to stubborn inflation. Goldman analysts predict one final increase before year-end pause."
    },
)
result = resp.json()

const resp = await fetch("https://api.factagora.com/api/v1/fingerprint/detect", {
  method: "POST",
  headers: {
    Authorization: "Bearer fa_your_api_key",
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    content:
      "ECB hiked rates 25bp Thursday due to stubborn inflation. Goldman analysts predict one final increase before year-end pause.",
  }),
});
const result = await resp.json();

Response

{
  "match_found": true,
  "confidence_score": 0.87,
  "query_fingerprint_id": "fp_tmpQueryId01",
  "matches": [
    {
      "fingerprint_id": "fp_l1p8OPCwGhvu",
      "content_type": "news",
      "metadata": { "author_id": "editor_jane", "source_id": "article_12345" },
      "embed_timestamp": "2024-06-15T08:01:23.456Z",
      "score": 0.87,
      "similarity_breakdown": {
        "entity_match": 0.92,
        "timeseries_match": 1.0,
        "causal_pattern_match": 0.78
      },
      "overlap": {
        "entities": ["european central bank", "interest rate", "goldman sachs"],
        "timeseries": ["2024-06-15"],
        "relations": ["european central bank|raises|interest rate"]
      },
      "watermark_match": false,
      "watermark_correlation": null
    }
  ],
  "meta": {
    "scanned": 156,
    "executionTimeMs": 342,
    "weights": { "entity": 0.5, "time": 0.2, "causal": 0.3 },
    "watermark_detected": false
  }
}

Notice that the query text is completely rewritten, “ECB hiked rates 25bp” vs “European Central Bank raised interest rates by 25 basis points”, yet the API matched it with 87% confidence by comparing the underlying causal structure.

3. Detect with watermark (highest confidence)

If the watermarked version of your content was copied with the invisible characters intact, the watermark layer fires for near-certain provenance:

{
  "matches": [
    {
      "fingerprint_id": "fp_l1p8OPCwGhvu",
      "score": 0.87,
      "watermark_match": true,
      "watermark_correlation": 0.98,
      ...
    }
  ],
  "meta": {
    "watermark_detected": true
  }
}

When watermark_match: true, the match is cryptographically verified, the exact watermarked content (or a portion of it) was used.

4. Tune detection with custom weights

Different content types benefit from different scoring emphasis. Override the defaults when needed:

{
  "content": "...",
  "weights": { "entity": 0.3, "time": 0.1, "causal": 0.6 }
}

{
  "content": "...",
  "weights": { "entity": 0.3, "time": 0.5, "causal": 0.2 }
}

The weights used are always echoed back in meta.weights so you can verify what was applied.

5. Re-score an existing fingerprint

To periodically check if new content matches a fingerprint you already embedded, pass the fingerprint_id directly:

curl -X POST "https://api.factagora.com/api/v1/fingerprint/detect" \
  -H "Authorization: Bearer fa_your_api_key" \
  -H "Content-Type: application/json" \
  -d '{"fingerprint_id": "fp_l1p8OPCwGhvu"}'

This reuses the stored TKG snapshot without re-extracting, making it faster and ideal for scheduled monitoring.

Summary

Step	Endpoint	What it does
Embed	`POST /fingerprint/embed`	Extracts TKG, embeds watermark, stores fingerprint
Detect	`POST /fingerprint/detect`	Runs watermark + TKG matching against stored fingerprints
Re-score	`POST /fingerprint/detect` (with `fingerprint_id`)	Re-scores existing fingerprint against new candidates

Overview

Factagora API

DeepVerify (Chrome extension)

DeepStamp

ChatGPT

1. Embed a watermark

Response

2. Detect content reuse

Response

3. Detect with watermark (highest confidence)

4. Tune detection with custom weights

5. Re-score an existing fingerprint

Summary

​1. Embed a watermark

​Response

​2. Detect content reuse

​Response

​3. Detect with watermark (highest confidence)

​4. Tune detection with custom weights

​5. Re-score an existing fingerprint

​Summary

1. Embed a watermark

Response

2. Detect content reuse

Response

3. Detect with watermark (highest confidence)

4. Tune detection with custom weights

5. Re-score an existing fingerprint

Summary