> ## Documentation Index
> Fetch the complete documentation index at: https://docs.factagora.com/llms.txt
> Use this file to discover all available pages before exploring further.

# DeepStamp

> Best practices for DeepStamp's Embed & Detect APIs

<Note>
  **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.
</Note>

## Embed at publish time, not retroactively

Watermark content as early as possible, ideally at the moment of publication or distribution. Embedding after content has already circulated reduces the provenance window.

```json theme={null}
{
  "content": "...",
  "content_type": "news",
  "metadata": {
    "published_at": "2024-03-15T09:00:00Z",
    "source_id": "article_98765"
  }
}
```

## Always store the fingerprint\_id

The `fingerprint_id` returned by `/embed` is your key for retrieval and detection. Store it alongside your internal document record.

```json theme={null}
{
  "internal_id": "article_98765",
  "fingerprint_id": "fp_l1p8OPCwGhvu"
}
```

## Distribute watermarked\_content, not the original

The `watermarked_content` in the response contains invisible zero-width Unicode characters. Distribute this version instead of the original, it is visually identical but carries the cryptographic watermark that enables detection.

## Choose the right content\_type

Scoring weights are automatically tuned per content type. Choosing the correct type improves detection accuracy:

| Content type     | Entity weight | Time weight | Causal weight | Best for                                  |
| ---------------- | ------------- | ----------- | ------------- | ----------------------------------------- |
| `news` (default) | 0.5           | 0.2         | 0.3           | News articles, press releases             |
| `legal`          | 0.3           | 0.1         | 0.6           | Legal documents, contracts, court filings |
| `report`         | 0.4           | 0.3         | 0.3           | Research reports, analysis, whitepapers   |
| `internal`       | 0.5           | 0.2         | 0.3           | Internal memos, communications            |

## Use custom weights for specialized use cases

Override the default weights when your detection scenario is unusual. For example, if you only care about whether the same entities appear (regardless of causal structure):

```json theme={null}
{
  "content": "...",
  "weights": { "entity": 0.8, "time": 0.1, "causal": 0.1 }
}
```

Weights must sum to `1.0`. The weights used are echoed back in `meta.weights` for auditability.

## Use filters to narrow detection scope

Pass `filters` to reduce the candidate set and speed up detection:

```json theme={null}
{
  "content": "...",
  "filters": {
    "author_id": "journalist_042",
    "date_from": "2024-01-01",
    "date_to": "2024-12-31",
    "content_type": "news"
  }
}
```

## Understand the two detection layers

Detection runs two independent checks, use both signals together:

| Layer           | How it works                                                                      | What it proves                                                    |
| --------------- | --------------------------------------------------------------------------------- | ----------------------------------------------------------------- |
| **Watermark**   | Extracts invisible zero-width bits and correlates them against stored seeds       | Near-certain provenance, the exact watermarked content was used   |
| **TKG Jaccard** | Compares entities, timelines, and argument chains using word-level fuzzy matching | Semantic similarity, the same story, even if completely rewritten |

Check `watermark_match` and `watermark_correlation` on each match to see if the watermark layer fired. The `meta.watermark_detected` field tells you whether any watermark was found in the input at all.

## Interpret confidence scores carefully

| Score range | Interpretation | Recommended action                       |
| ----------- | -------------- | ---------------------------------------- |
| 0.8 – 1.0   | Strong match   | High confidence, safe to automate        |
| 0.5 – 0.8   | Partial match  | Review the `overlap` lists before acting |
| 0.3 – 0.5   | Weak signal    | Likely coincidental overlap              |
| Below 0.3   | Filtered out   | Not returned (below default `min_score`) |

When `watermark_match: true`, the match is near-certain regardless of the TKG score.

## Audit matches with overlap lists

Every match includes `overlap.entities`, `overlap.timeseries`, and `overlap.relations`, the specific items shared between the query and the candidate. Use these to explain *why* two articles matched, not just *that* they matched.

```json theme={null}
{
  "overlap": {
    "entities": ["bank of korea", "interest rate"],
    "timeseries": ["2024-03-15"],
    "relations": ["bank of korea|raises|interest rate"]
  }
}
```

## Use fingerprint\_id for re-scoring

If you've already embedded content and want to re-score it against the registry (e.g., periodically checking for new matches), pass the `fingerprint_id` directly instead of the content:

```json theme={null}
{
  "fingerprint_id": "fp_l1p8OPCwGhvu"
}
```

This skips content extraction and uses the stored TKG snapshot, making it faster and idempotent.

## Combine with other Factagora APIs

| Workflow                   | APIs                                                                 | Purpose                   |
| -------------------------- | -------------------------------------------------------------------- | ------------------------- |
| Was my content reused?     | DeepStamp Detect                                                     | Provenance tracking       |
| Was it reused accurately?  | DeepStamp Detect + [Fact Checker](/api-reference/fact-checker)       | Detect misrepresentation  |
| What changed in the story? | DeepStamp Detect + [Causality Graph](/api-reference/causality-graph) | Track narrative evolution |