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

# Interpreting Detection Results

> Understanding deepfake detection verdicts, risk scores, and layer results

> A detailed guide to reading and acting on deepfake detection results — from verdict outcomes and risk scores to per-layer analysis and session status impact.

## Where Results Appear

Deepfake detection results are included in the session's `analysis_data` object. You can access them through three channels:

* **Retrieve Session API** — call the [Retrieve Session](/api-reference/sessions/retrieve-session) endpoint to programmatically access detection data for any completed session.
* **Webhook events** — `session.status.verified`, `session.status.rejected`, and `session.status.submitted` payloads include the full `deepfake_detection_data` object.
* **Admin Console** — navigate to a session in the Admin Console to view detection results alongside all other verification data.

***

## Response Structure

When Deepfake Detection is enabled in a workflow, the session's `analysis_data` object includes a `deepfake_detection_data` field:

```json theme={null}
{
  "deepfake_detection_data": {
    "verdict": "LIVE",
    "risk_score": 18,
    "confidence": 92,
    "avg_layer_confidence": 91,
    "layer_results": {
      "liveness": {
        "passed": true,
        "checks": {
          "landmark_detection": true,
          "blink_detection": true,
          "head_movement": true,
          "frame_timing": true
        }
      },
      "vision": {
        "risk_score": 22,
        "confidence": 90,
        "signals": []
      },
      "voice": {
        "risk_score": 13,
        "confidence": 88,
        "word_match": true
      }
    },
    "analyzed_at": "2025-01-15T10:43:00.000Z"
  }
}
```

### Top-Level Fields

| Field                  | Type           | Description                                                                                                                                          |
| ---------------------- | -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------- |
| `verdict`              | string         | Final determination: `LIVE` (authentic), `DEEPFAKE` (synthetic content detected), or `INCONCLUSIVE` (insufficient confidence for a definitive call). |
| `risk_score`           | number (0–100) | Combined risk score across all analysis layers. Higher values indicate greater likelihood of synthetic content.                                      |
| `confidence`           | number (0–100) | Overall confidence in the verdict. Higher values indicate more certainty in the determination.                                                       |
| `avg_layer_confidence` | number (0–100) | Average confidence across the AI analysis layers (vision and voice).                                                                                 |
| `layer_results`        | object         | Per-layer breakdown of the analysis. Contains `liveness`, `vision`, and `voice` sub-objects.                                                         |
| `analyzed_at`          | string         | ISO 8601 timestamp indicating when the analysis was performed.                                                                                       |

***

## Layer Results

The `layer_results` object provides granular results from each of the three analysis layers that run in parallel during detection.

### Liveness Layer

The liveness layer validates that a physically present person performed the required actions. It acts as a **binary gate** — if any check fails, the verdict is immediately `DEEPFAKE` regardless of other layer results.

| Field                       | Type    | Description                                                    |
| --------------------------- | ------- | -------------------------------------------------------------- |
| `passed`                    | boolean | Whether all liveness checks passed.                            |
| `checks.landmark_detection` | boolean | Valid facial landmarks detected across all captured frames.    |
| `checks.blink_detection`    | boolean | A real blink was confirmed through eye aspect ratio variance.  |
| `checks.head_movement`      | boolean | Real left/right head turns detected through positional shifts. |
| `checks.frame_timing`       | boolean | Frame capture timing falls within expected parameters.         |

### Vision Layer

The vision layer analyses the captured frames for visual deepfake artifacts using AI-powered inspection.

| Field        | Type           | Description                                                                               |
| ------------ | -------------- | ----------------------------------------------------------------------------------------- |
| `risk_score` | number (0–100) | Vision-specific risk score. Higher values indicate more visual deepfake signals detected. |
| `confidence` | number (0–100) | Confidence in the vision analysis based on clarity of signal detection.                   |
| `signals`    | array          | List of specific visual anomalies detected. Empty when no anomalies are found.            |

### Voice Layer

The voice layer analyses the audio clip captured during the session, including verification that the applicant spoke the challenge phrase correctly.

| Field        | Type           | Description                                                                                                                              |
| ------------ | -------------- | ---------------------------------------------------------------------------------------------------------------------------------------- |
| `risk_score` | number (0–100) | Voice-specific risk score. Higher values indicate more synthetic voice signals detected.                                                 |
| `confidence` | number (0–100) | Confidence in the voice analysis based on audio quality and clarity of signals.                                                          |
| `word_match` | boolean        | Whether the applicant correctly spoke the challenge phrase. A `false` value (wrong phrase or no speech) results in a `DEEPFAKE` verdict. |

***

## Vision Signals

When visual anomalies are identified, the `vision.signals` array contains one or more signal identifiers:

| Signal                 | Description                                                                                                                                           |
| ---------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------- |
| `TEXTURE_SMOOTHING`    | Face region appears unnaturally smoother than surrounding skin (neck, ears) — a characteristic artifact of neural network face generation.            |
| `BOUNDARY_ARTIFACTS`   | Visible seam or boundary detected where the face meets the head/background — indicates a face-swap blend region.                                      |
| `BLINK_FAILURE`        | Eyes did not fully close during the blink frame. Face-swap models struggle to render complete eye closure.                                            |
| `MOUTH_ANOMALY`        | Mouth not visibly open during the speaking frame, suggesting audio was played over a static or minimally animated face.                               |
| `TEETH_RENDERING`      | Unnatural teeth appearance — face-swap models typically render teeth as a blurry white mass rather than distinct, detailed teeth.                     |
| `FLAT_ROTATION`        | Head rotation appears flat without natural 3D parallax. Face swaps rotate the face as a 2D texture rather than a three-dimensional object.            |
| `TEMPORAL_INSTABILITY` | Facial features are inconsistent across frames. Face-swap models regenerate features per-frame rather than maintaining persistent geometry.           |
| `SKIN_TONE_MISMATCH`   | Face skin tone does not match the neck/body skin tone — a colour temperature mismatch caused by the source face having different lighting conditions. |

***

## Verdict Logic

The verdict engine evaluates results in priority order:

| Priority | Condition                                                            | Verdict        |
| -------- | -------------------------------------------------------------------- | -------------- |
| 1        | Liveness check failed (any `checks` value is `false`)                | `DEEPFAKE`     |
| 2        | Word mismatch — applicant did not speak the correct challenge phrase | `DEEPFAKE`     |
| 3        | Vision risk score exceeds the deepfake threshold                     | `DEEPFAKE`     |
| 4        | Voice risk score exceeds the synthetic speech threshold              | `DEEPFAKE`     |
| 5        | Average layer confidence is below the minimum confidence threshold   | `INCONCLUSIVE` |
| 6        | None of the above conditions are met                                 | `LIVE`         |

<Info>
  `DEEPFAKE` verdicts trigger regardless of confidence level — the system is calibrated to prioritize preventing deepfake fraud over avoiding false positives. In the rare case of a false positive, applicants can retry the verification session.
</Info>

***

## Impact on Session Status

The deepfake detection verdict directly influences the session's final status:

* **`DEEPFAKE` → Session `REJECTED`** — The session automatically transitions to `REJECTED`. This prevents deepfake-based fraud from progressing through the verification pipeline.
* **`INCONCLUSIVE` → Manual review** — The session remains in `SUBMITTED` status, flagged for manual review in the Admin Console. This occurs when the system does not have sufficient confidence to make a definitive determination (e.g., low webcam quality, poor lighting). A compliance analyst can examine the layer results and make a final call.
* **`LIVE` → Normal processing** — The session continues through normal verification processing. Other workflow steps (ID Verification, PEP & Sanctions, etc.) proceed as configured.

***

## Example: Deepfake Detected

The following response illustrates a session where a face-swap deepfake was identified with high confidence:

```json theme={null}
{
  "deepfake_detection_data": {
    "verdict": "DEEPFAKE",
    "risk_score": 78,
    "confidence": 91,
    "avg_layer_confidence": 90,
    "layer_results": {
      "liveness": {
        "passed": true,
        "checks": {
          "landmark_detection": true,
          "blink_detection": true,
          "head_movement": true,
          "frame_timing": true
        }
      },
      "vision": {
        "risk_score": 82,
        "confidence": 90,
        "signals": [
          "TEXTURE_SMOOTHING",
          "BOUNDARY_ARTIFACTS",
          "TEETH_RENDERING",
          "SKIN_TONE_MISMATCH"
        ]
      },
      "voice": {
        "risk_score": 13,
        "confidence": 88,
        "word_match": true
      }
    },
    "analyzed_at": "2025-01-15T14:22:31.000Z"
  }
}
```

In this case, the liveness checks passed (the deepfake software successfully replicated basic movements), but the vision layer detected four distinct artifact signals — texture smoothing, boundary seams, unnatural teeth, and a skin tone mismatch between the face and neck. The vision risk score of 82 exceeded the deepfake threshold, resulting in a `DEEPFAKE` verdict. The voice layer showed no synthetic indicators, confirming this was a face-swap attack rather than a fully synthetic session.

***

## Example: Inconclusive Result

When the system cannot make a definitive determination, typically due to poor capture conditions:

```json theme={null}
{
  "deepfake_detection_data": {
    "verdict": "INCONCLUSIVE",
    "risk_score": 41,
    "confidence": 55,
    "avg_layer_confidence": 55,
    "layer_results": {
      "liveness": {
        "passed": true,
        "checks": {
          "landmark_detection": true,
          "blink_detection": true,
          "head_movement": true,
          "frame_timing": true
        }
      },
      "vision": {
        "risk_score": 45,
        "confidence": 50,
        "signals": []
      },
      "voice": {
        "risk_score": 36,
        "confidence": 60,
        "word_match": true
      }
    },
    "analyzed_at": "2025-01-15T16:05:12.000Z"
  }
}
```

The average layer confidence of 55 fell below the minimum threshold, so the system returned `INCONCLUSIVE` rather than making a potentially incorrect determination. This session would be flagged for manual review.

<Info>
  Deepfake detection data is included in webhook event payloads when session status changes. Configure your webhook endpoint under **Settings > API Keys** to receive real-time notifications. See [Webhooks](/webhooks/overview) for setup details.
</Info>