> ## 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.

# Live Session Deepfake Detection

> Detect real-time deepfake attacks during identity verification sessions

> Identify AI face-swapping, synthetic overlays, and deepfake masks in real time as applicants complete identity verification — adding a critical layer of defence to your onboarding process.

export const SectionHeader = ({label, title, description, align = "left"}) => <div className="deepidv-section-header" style={{
  textAlign: align,
  alignItems: align === "center" ? "center" : "flex-start"
}}>
    {label && <p className="deepidv-section-label">{label}</p>}
    <h2 className="deepidv-section-title">{title}</h2>
    {description && <p className="deepidv-section-desc">{description}</p>}
  </div>;

export const FeatureGrid = ({cols = 3, children}) => <div className="deepidv-feature-grid" style={{
  "--grid-cols": cols
}}>
    {children}
  </div>;

export const FeatureCard = ({icon, title, description, href}) => {
  const inner = <div className="deepidv-feature-card">
      {icon && <div className="deepidv-feature-card-icon">
          <Icon icon={icon} size={20} />
        </div>}
      <div className="deepidv-feature-card-content">
        <h3 className="deepidv-feature-card-title">{title}</h3>
        {description && <p className="deepidv-feature-card-desc">{description}</p>}
      </div>
      {href && <div className="deepidv-feature-card-arrow">
          <Icon icon="arrow-right" size={14} />
        </div>}
    </div>;
  if (href) {
    return <a href={href} style={{
      textDecoration: "none",
      color: "inherit",
      display: "block",
      border: "none",
      boxShadow: "none"
    }}>
        {inner}
      </a>;
  }
  return inner;
};

## What is Live Deepfake Detection?

Live Deepfake Detection is a dedicated workflow step that analyses both the video frames and audio captured during an identity verification session. It runs three independent analysis layers in parallel — liveness verification, vision-based deepfake analysis, and voice verification — to produce a final verdict of **LIVE**, **DEEPFAKE**, or **INCONCLUSIVE**.

The system captures 6 frames and an audio clip during the session. The applicant is presented with a **challenge phrase** (3 random words) that they must speak aloud, enabling the voice verification layer to detect synthetic speech and text-to-speech attacks alongside the visual analysis.

While Face Liveness confirms a real, physically present person is in front of the camera, Deepfake Detection goes further — it determines whether the face being presented is authentic or AI-generated, and whether the voice is a real human voice or a synthetic reproduction. Together, these layers provide comprehensive protection against face-swap deepfakes, presentation attacks, and synthetic voice attacks.

***

<SectionHeader
  label="Detection Pipeline"
  title={
<>
  How It <span>Works</span>
</>
}
/>

<Steps>
  <Step title="Session initiated">
    The applicant begins their identity verification session through the hosted verification link or embedded flow.
  </Step>

  <Step title="Frame and audio capture">
    The applicant's device captures **6 video frames** at key moments — a still frame, a blink, left and right head turns, and a speaking frame — along with an **audio clip** of the applicant reading the challenge phrase aloud. All data is transmitted securely to deepidv's analysis infrastructure.
  </Step>

  <Step title="Three-layer parallel analysis">
    Three independent analysis layers run simultaneously for a total processing time of 5–7 seconds:

    * **Liveness verification** — validates facial landmark data across all frames, confirming real blinks, head turns, and proper frame timing to rule out photo/video replay attacks
    * **Vision analysis** — AI-powered inspection of the captured frames for face-swap artifacts including boundary seams, texture smoothing, unnatural teeth rendering, flat rotation without depth, temporal instability across frames, and skin tone mismatches
    * **Voice verification** — analyses the audio for biological sounds (breathing, lip smacks), natural voice onset/offset patterns, consonant variation, pitch micro-tremors, and replay artifacts to detect text-to-speech and voice cloning attacks
  </Step>

  <Step title="Verdict determination">
    A verdict engine combines the results from all three layers into a final determination: **LIVE** (authentic), **DEEPFAKE** (synthetic content detected), or **INCONCLUSIVE** (insufficient confidence to make a definitive call). A combined `risk_score` and `confidence` value accompany the verdict.
  </Step>

  <Step title="Results recorded">
    Detection results are included in the session's `analysis_data` object as `deepfake_detection_data`, available via the [Retrieve Session](/api-reference/sessions/retrieve-session) endpoint, webhook events, and visible in the Admin Console.
  </Step>
</Steps>

***

## Enabling Deepfake Detection

### Via the Admin Console

1. Navigate to **Workflows** in the sidebar
2. Select an existing workflow or click **Create New** to build one
3. Locate the **Deepfake Detection** toggle in the workflow configuration
4. Enable the toggle to add deepfake detection to the workflow
5. Click **Save** to apply changes

All future sessions created with this workflow will automatically include deepfake detection analysis.

### Via the API

When creating a session programmatically, reference a `workflowId` that has Deepfake Detection enabled:

<CodeGroup>
  ```bash cURL theme={null}
  curl -X POST https://api.deepidv.com/v1/sessions \
    -H "Content-Type: application/json" \
    -H "x-api-key: YOUR_API_KEY" \
    -d '{
      "firstName": "Jane",
      "lastName": "Smith",
      "email": "jane.smith@example.com",
      "phone": "+14165557890",
      "workflowId": "your-deepfake-enabled-workflow-id"
    }'
  ```

  ```javascript Node.js theme={null}
  const response = await fetch("https://api.deepidv.com/v1/sessions", {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
      "x-api-key": "YOUR_API_KEY",
    },
    body: JSON.stringify({
      firstName: "Jane",
      lastName: "Smith",
      email: "jane.smith@example.com",
      phone: "+14165557890",
      workflowId: "your-deepfake-enabled-workflow-id",
    }),
  });

  const session = await response.json();
  console.log(session);
  ```

  ```python Python theme={null}
  import requests

  response = requests.post(
      "https://api.deepidv.com/v1/sessions",
      headers={
          "Content-Type": "application/json",
          "x-api-key": "YOUR_API_KEY",
      },
      json={
          "firstName": "Jane",
          "lastName": "Smith",
          "email": "jane.smith@example.com",
          "phone": "+14165557890",
          "workflowId": "your-deepfake-enabled-workflow-id",
      },
  )

  print(response.json())
  ```
</CodeGroup>

<Note>
  Find your `workflowId` under **Workflows** in the Admin Console sidebar. Ensure the workflow has the Deepfake Detection toggle enabled before creating sessions.
</Note>

***

<SectionHeader
  label="Applications"
  title={
<>
  Banking <span>Use Cases</span>
</>
}
/>

<FeatureGrid cols={3}>
  <FeatureCard icon="user-plus" title="Remote Customer Onboarding" description="Protect digital account opening channels from synthetic identity fraud. Ensure every new customer is who they claim to be, satisfying KYC requirements from the outset." />

  <FeatureCard icon="money-check-dollar" title="High-Value Transaction Authorization" description="Add deepfake detection as an additional verification layer for large transfers, wire authorizations, and treasury operations that exceed defined risk thresholds." />

  <FeatureCard icon="rotate" title="Account Recovery Verification" description="When customers request account recovery or credential resets, confirm their identity with deepfake-resistant verification to prevent account takeover." />
</FeatureGrid>

<Tip>
  For maximum protection, combine Deepfake Detection with Face Liveness and ID Verification in a single workflow. This layered approach addresses presentation attacks, digital injection, and document fraud simultaneously.
</Tip>

***

<CardGroup cols={2}>
  <Card title="Interpreting Results" icon="chart-simple" href="/deepfake-detection/live-session-results">
    Understand deepfake detection scores, signals, and confidence thresholds.
  </Card>

  <Card title="Workflows" icon="diagram-project" href="/workflows/workflows">
    Learn how to build and manage verification workflows with deepfake detection.
  </Card>
</CardGroup>