DeepStamp

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.",
    "content_type": "news",
    "metadata": {
      "author_id": "editor_jane",
      "published_at": "2024-06-15T08:00:00Z",
      "source_id": "article_12345"
    }
  }'

{
  "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 }
}

POST

api

fingerprint

embed

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.",
    "content_type": "news",
    "metadata": {
      "author_id": "editor_jane",
      "published_at": "2024-06-15T08:00:00Z",
      "source_id": "article_12345"
    }
  }'

{
  "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 }
}

DeepStamp proves that a piece of content originated from you, even after it has been rewritten or translated. Embedding extracts a Temporal Knowledge Graph (TKG) from your content and hides a watermark seeded by that structure. Detection scores candidate content against every stored fingerprint using both the watermark and the causal structure.

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.

Endpoints

Method	Path	Purpose
`POST`	`/api/v1/fingerprint/embed`	Extract a TKG, embed the watermark, store the fingerprint
`POST`	`/api/v1/fingerprint/detect`	Score content against stored fingerprints

POST /api/v1/fingerprint/embed

Request Body

Provide either content or url, not both.

string

Raw text to fingerprint. Maximum 50,000 characters.

string

URL of the source document to fingerprint.

string

default:"news"

Category of the content. One of news, report, legal, internal. This also selects the default scoring weights used at detection time.

object

Your own identifiers, stored with the fingerprint and echoed back on every match.

Show Metadata fields

string

Internal author identifier

string

ISO 8601 publish timestamp

string

Upstream source identifier, such as a CMS article id

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.",
    "content_type": "news",
    "metadata": {
      "author_id": "editor_jane",
      "published_at": "2024-06-15T08:00:00Z",
      "source_id": "article_12345"
    }
  }'

Response

string

Your content with the TKG-seeded zero-width watermark embedded. Distribute this version, not the original.

string

Unique fingerprint identifier (fp_*). Store it alongside your internal article id.

object

The structure extracted from your content.

Show Snapshot fields

array

Distinct entities found in the content

array

Normalized time anchors (YYYY, YYYY-MM, or YYYY-MM-DD)

array

Causal or temporal relations as from / rel / to triples

array

Premise → evidence → conclusion chains

string

ISO 8601 timestamp of when the fingerprint was embedded.

{
  "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 }
}

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
  }
}

Overview

Endpoints

Best Practices

Endpoints

POST /api/v1/fingerprint/embed

Request Body

Response

POST /api/v1/fingerprint/detect

Request Body

Response

Next steps

Embed & Detect walkthrough

Best practices

​Endpoints

​POST /api/v1/fingerprint/embed

​Request Body

​Response

​POST /api/v1/fingerprint/detect

​Request Body

​Response

​Next steps

Embed & Detect walkthrough

Best practices

Endpoints

POST /api/v1/fingerprint/embed

Request Body

Response

POST /api/v1/fingerprint/detect

Request Body

Response

Next steps