DeepStamp 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.",
    "top_k": 5,
    "min_score": 0.3
  }'

{
  "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
  }
}

POST

api

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.",
    "top_k": 5,
    "min_score": 0.3
  }'

{
  "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
  }
}

Detection scores candidate content against every stored fingerprint, using both the hidden watermark and the causal structure of the text. It catches reuse even when every word has been changed.

DeepStamp is the product name; the API paths still say fingerprint. Endpoints are /api/v1/fingerprint/embed and /api/v1/fingerprint/detect, and responses return fingerprint_id. There is no /api/v1/deepstamp/* endpoint.

POST /api/v1/fingerprint/detect

Request Body

Provide exactly one of content, url, or fingerprint_id. Content and URL queries are extracted on the fly and are never stored.

string

Raw text to scan, typically the article you want to check. Maximum 50,000 characters.

string

URL of the document to scan.

string

An already-embedded fingerprint id (fp_*) to re-score against the registry. This reuses the stored snapshot instead of re-extracting, which makes it the cheapest option for scheduled monitoring.

number

default:"5"

Number of matches to return. Range: 1–50.

number

default:"0.3"

Minimum combined score for a candidate to be reported. Candidates with no shared signal at all are always excluded, whatever this value is. match_found is true only when at least one match clears both gates.

number

default:"1000"

Upper bound on how many stored fingerprints are scanned, newest first. Range: 1–5000.

object

Custom scoring weights. entity, time, and causal must sum to 1.0. When omitted, content-type defaults apply: news 0.5 / 0.2 / 0.3, legal 0.3 / 0.1 / 0.6, report 0.4 / 0.3 / 0.3, internal 0.5 / 0.2 / 0.3.

object

Restrict which stored fingerprints are considered.

Show Filter fields

string

Only this author’s fingerprints

string

Inclusive lower bound on embed_timestamp (YYYY-MM-DD)

string

Inclusive upper bound on embed_timestamp (YYYY-MM-DD)

string

Only one content type

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.",
    "top_k": 5,
    "min_score": 0.3
  }'

Response

boolean

True when at least one candidate cleared min_score.

number

The top match’s combined score, or 0 when nothing matched.

string

The query’s fingerprint id. For content and url queries this is minted during on-the-fly extraction and is not persisted.

array

Top-K candidates, ranked by combined score descending.

Show Match fields

string

Matched fingerprint id

string

Content type recorded at embed time

object

Metadata stored at embed time, may be null

string

When the candidate was fingerprinted

number

Weighted combined score

object

Per-signal scores: entity_match, timeseries_match, causal_pattern_match

object

The shared entities, time anchors, and from|rel|to triples behind the score

boolean

True when the input carries a watermark correlating 85% or higher with this candidate. This is near-certain provenance evidence

number

Bit-level correlation, null when no watermark was detected

object

Show Meta fields

number

How many stored fingerprints were scored after filters

number

Response time in milliseconds

object

The weights actually applied to this request

boolean

True when a watermark was found in the input and matched a stored fingerprint

{
  "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
  }
}

Embedding is covered in DeepStamp Embed.

Overview

Endpoints

Best Practices

POST /api/v1/fingerprint/detect

Request Body

Response

​POST /api/v1/fingerprint/detect

​Request Body

​Response

POST /api/v1/fingerprint/detect

Request Body

Response