Resemble Detect Davepoon

---
name: resemble-detect
description: "Deepfake detection and media safety — detect AI-generated audio, images, video, and text, trace synthesis sources, apply watermarks, verify speaker identity, and analyze media intelligence using Resemble AI"
category: media-and-creative
source_repo: davepoon/buildwithclaude
source_path: "plugins/all-skills/skills/resemble-detect/SKILL.md"
source_url: https://github.com/davepoon/buildwithclaude/blob/HEAD/plugins/all-skills/skills/resemble-detect/SKILL.md
---


# Resemble Detect — Deepfake Detection & Media Safety

Analyze audio, image, video, and text for synthetic manipulation, AI-generated content, watermarks, speaker identity, and media intelligence using the Resemble AI platform.

## Core Principle — THE IRON LAW

**"NEVER DECLARE MEDIA AS REAL OR FAKE WITHOUT A COMPLETED DETECTION RESULT."**

Do not guess, infer, or speculate about media authenticity. Every authenticity claim must be backed by a completed Resemble detect job with a returned `label`, `score`, and `status: "completed"`. If the detection is still `processing`, wait. If it `failed`, say so — do not substitute your own judgment.

## When to Use

Use this skill whenever the user's request involves any of these:

- Checking if audio, video, image, or text is AI-generated or manipulated
- Detecting deepfakes in any media format
- Verifying media authenticity or provenance
- Identifying which AI platform synthesized audio (source tracing)
- Applying or detecting watermarks on media
- Analyzing media for speaker info, emotion, transcription, or misinformation
- Asking natural-language questions about detection results
- Matching or verifying speaker identity against known voice profiles
- Detecting AI-generated or machine-written text
- Any mention of: "deepfake", "fake detection", "synthetic media", "voice verification", "watermark", "media forensics", "authenticity check", "source tracing", "is this real", "AI-written text", "text detection"

**Do NOT use** for text-to-speech generation, voice cloning, or speech-to-text transcription — those are separate Resemble capabilities.

## Capability Decision Tree

| User wants to...                                      | Use this                  | API endpoint               |
|-------------------------------------------------------|---------------------------|----------------------------|
| Check if media is AI-generated / deepfake             | **Deepfake Detection**    | `POST /detect`             |
| Know *which AI platform* made fake audio              | **Audio Source Tracing**   | `POST /detect` with flag   |
| Get speaker info, emotion, transcription from media   | **Intelligence**          | `POST /intelligence`       |
| Ask questions about a completed detection             | **Detect Intelligence**   | `POST /detects/{uuid}/intelligence` |
| Apply an invisible watermark to media                 | **Watermark Apply**       | `POST /watermark/apply`    |
| Check if media contains a watermark                   | **Watermark Detect**      | `POST /watermark/detect`   |
| Verify a speaker's identity against known profiles    | **Identity Search**       | `POST /identity/search`    |
| Check if text is AI-generated                         | **Text Detection**        | `POST /text_detect`        |
| Create a voice identity profile for future matching   | **Identity Create**       | `POST /identity`           |

When multiple capabilities apply (e.g., user wants deepfake detection AND intelligence), combine them in a single `POST /detect` call using the `intelligence: true` flag rather than making separate requests.

## Required Setup

- **API Key**: Bearer token from the Resemble AI dashboard
- **Base URL**: `https://app.resemble.ai/api/v2`
- **Auth Header**: `Authorization: Bearer <RESEMBLE_API_KEY>`
- **Media Requirement**: All media must be at a publicly accessible HTTPS URL

If the user provides a local file path instead of a URL, inform them the file must be hosted at a public HTTPS URL first. Do not attempt to upload local files to the API.

## MCP Tools Available

When the Resemble MCP server is connected, use these tools instead of raw API calls:

| Tool                      | Purpose                                           |
|---------------------------|---------------------------------------------------|
| `resemble_docs_lookup`    | Get comprehensive docs for any detect sub-topic   |
| `resemble_search`         | Search across all documentation                   |
| `resemble_api_endpoint`   | Get exact OpenAPI spec for any endpoint            |
| `resemble_api_search`     | Find endpoints by keyword                         |
| `resemble_get_page`       | Read specific documentation pages                 |
| `resemble_list_topics`    | List all available topics                         |

**Tool usage pattern**: Use `resemble_docs_lookup` with topic `"detect"` to get the full picture, then `resemble_api_endpoint` for exact request/response schemas before making API calls.

---

## Phase 1: Deepfake Detection

The core capability. Submit any audio, image, or video for AI-generated content analysis.

### Submit a Detection

```
POST /detect
Content-Type: application/json
Authorization: Bearer <API_KEY>

{
  "url": "https://example.com/media.mp4",
  "visualize": true,
  "intelligence": true,
  "audio_source_tracing": true
}
```

**Parameters:**

| Parameter              | Type    | Required | Description                                              |
|------------------------|---------|----------|----------------------------------------------------------|
| `url`                  | string  | Yes      | HTTPS URL to audio, image, or video file                 |
| `callback_url`         | string  | No       | Webhook URL for async completion notification             |
| `visualize`            | boolean | No       | Generate heatmap/visualization artifacts                  |
| `intelligence`         | boolean | No       | Run multimodal intelligence analysis alongside detection  |
| `audio_source_tracing` | boolean | No       | Identify which AI platform synthesized fake audio         |
| `frame_length`         | integer | No       | Audio/video analysis window size in seconds (1–4, default 2) |
| `start_region`         | number  | No       | Start of segment to analyze (seconds)                    |
| `end_region`           | number  | No       | End of segment to analyze (seconds)                      |
| `model_types`          | string  | No       | `"image"` or `"talking_head"` (for face-swap detection)  |
| `use_reverse_search`   | boolean | No       | Enable reverse image search (image only)                 |
| `use_ood_detector`     | boolean | No       | Enable out-of-distribution detection                     |
| `zero_retention_mode`  | boolean | No       | Auto-delete media after detection completes              |

**Supported formats:**
- Audio: WAV, MP3, OGG, M4A, FLAC
- Video: MP4, MOV, AVI, WMV
- Image: JPG, PNG, GIF, WEBP

### Poll for Results

Detection is asynchronous. Poll `GET /detect/{uuid}` until `status` is `"completed"` or `"failed"`.

```
GET /detect/{uuid}
Authorization: Bearer <API_KEY>
```

**Polling best practice:** Start at 2s intervals, back off to 5s, then 10s. Most detections complete within 10–60 seconds depending on media length.

### Reading Results by Media Type

**Audio results** — in `metrics`:
```json
{
  "label": "fake",
  "score": ["0.92", "0.88", "0.95"],
  "consistency": "0.91",
  "aggregated_score": "0.92",
  "image": "https://..."
}
```
- `label`: `"fake"` or `"real"` — the verdict
- `score`: Per-chunk prediction scores (array)
- `aggregated_score`: Overall confidence (0.0–1.0, higher = more likely synthetic)
- `consistency`: How consistent the prediction is across chunks
- `image`: Visualization heatmap URL (if `visualize: true`)

**Image results** — in `image_metrics`:
```json
{
  "type": "ImageAnalysis",
  "label": "fake",
  "score": 0.87,
  "image": "https://...",
  "ifl": { "score": 0.82, "heatmap": "https://..." },
  "reverse_image_search_sources": [
    { "url": "...", "title": "...", "verdict": "known_fake", "similarity": 0.95 }
  ]
}
```
- `label` / `score`: Verdict and confidence
- `ifl`: Invisible Frequency Layer analysis with heatmap
- `reverse_image_search_sources`: Known sources found online (if `use_reverse_search: true`)

**Video results** — in `video_metrics`:
```json
{
  "label": "fake",
  "score": 0.89,
  "certainty": 0.91,
  "children": [
    {
      "type": "VideoResult",
      "conclusion": "Fake",
      "score": 0.89,
      "timestamp": 2.5,
      "children": [...]
    }
  ]
}
```
- Hierarchical tree of frame-level and segment-level results
- Each child has `timestamp`, `score`, `certainty`, and may have nested `children`
- Video with audio track returns both `metrics` (audio) and `video_metrics` (visual)

### Interpreting Scores

| Score Range | Interpretation                                      |
|-------------|-----------------------------------------------------|
| 0.0 – 0.3  | Strong indication of authentic/real media            |
| 0.3 – 0.5  | Inconclusive — recommend additional analysis         |
| 0.5 – 0.7  | Likely synthetic — flag for review                   |
| 0.7 – 1.0  | High confidence synthetic/AI-generated               |

**Always present scores with context.** Say "The detection returned a score of 0.87, indicating high confidence that this audio is AI-generated" — never just "it's fake."

---

## Phase 2: Intelligence — Media Analysis

Analyze media for rich structured insights independent of or alongside detection.

### Standalone Intelligence

```
POST /intelligence
Content-Type: application/json
Authorization: Bearer <API_KEY>

{
  "url": "https://example.com/audio.mp3",
  "json": true
}
```

**Parameters:**

| Parameter      | Type    | Required | Description                                              |
|----------------|---------|----------|----------------------------------------------------------|
| `url`          | string  | One of   | HTTPS URL to media file                                  |
| `media_token`  | string  | One of   | Token from secure upload (alternative to URL)             |
| `detect_id`    | string  | No       | UUID of existing detect to associate                      |
| `media_type`   | string  | No       | `"audio"`, `"video"`, or `"image"` (auto-detected)       |
| `json`         | boolean | No       | Return structured fields (default: false for audio/video, true for image) |
| `callback_url` | string  | No       | Webhook for async mode                                    |

**Audio/Video structured response** (`json: true`):
- `speaker_info` — speaker description (age, gender)
- `language` / `dialect` — detected language
- `emotion` — detected emotional state
- `speaking_style` — conversational, formal, etc.
- `context` — inferred context of the speech
- `message` — content summary
- `abnormalities` — anomalies detected in the media
- `transcription` — full transcript
- `translation` — translation if non-English
- `misinformation` — misinformation analysis

**Image structured response:**
- `scene_description` — what the image shows
- `subjects` — people/objects identified
- `authenticity_analysis` — visual authenticity assessment
- `context_and_setting` — environment description
- `abnormalities` — visual anomalies
- `misinformation` — misinformation analysis

### Detect Intelligence — Ask Questions About Results

After a detection completes, ask natural-language questions about it:

```
POST /detects/{detect_uuid}/intelligence
Content-Type: application/json
Authorization: Bearer <API_KEY>

{
  "query": "How confident is the model that this audio is fake?"
}
```

This returns a question UUID. Poll `GET /detects/{detect_uuid}/intelligence/{question_uuid}` until `status` is `"completed"` to get the `answer`.

**Good questions to suggest:**
- "Summarize the detection results in plain language"
- "What specific indicators suggest this is AI-generated?"
- "How do the audio and video detection results differ?"
- "What is the confidence level and what does it mean?"
- "Are there any inconsistencies in the analysis?"

**Status flow:** `pending` → `processing` → `completed` (or `failed`)

**Prerequisite:** The detection must have `status: "completed"`. Submitting a question against a processing or failed detection returns a 422 error.

---

## Phase 3: Audio Source Tracing

When audio is detected as synthetic (`label: "fake"`), identify which AI platform generated it.

**Enable it** by setting `audio_source_tracing: true` in the `POST /detect` request.

**Result** appears in the detection response under `audio_source_tracing`:
```json
{
  "label": "elevenlabs",
  "error_message": null
}
```

Known source labels include: `resemble_ai`, `elevenlabs`, `real`, and others as the model expands.

**Important:** Source tracing only runs when audio is labeled as `"fake"`. If the audio is `"real"`, no source tracing result will appear.

**Standalone query:**
- `GET /audio_source_tracings` — list all source tracing reports
- `GET /audio_source_tracings/{uuid}` — get specific report

---

## Phase 4: Watermarking

Apply invisible watermarks to media for provenance tracking, or detect existing watermarks.

### Apply a Watermark

```
POST /watermark/apply
Content-Type: application/json
Authorization: Bearer <API_KEY>
Prefer: wait

{
  "url": "https://example.com/image.png",
  "strength": 0.3,
  "custom_message": "my-organization"
}
```

| Parameter        | Type   | Required | Description                                          |
|------------------|--------|----------|------------------------------------------------------|
| `url`            | string | Yes      | HTTPS URL to media file                              |
| `strength`       | number | No       | Watermark strength 0.0–1.0 (image/video only, default 0.2) |
| `custom_message` | string | No       | Custom message to embed (image/video only, default "resembleai") |

- Add `Prefer: wait` header for synchronous response
- Without it, poll `GET /watermark/apply/{uuid}/result`
- Response includes `watermarked_media` URL to download the watermarked file

### Detect a Watermark

```
POST /watermark/detect
Content-Type: application/json
Authorization: Bearer <API_KEY>
Prefer: wait

{
  "url": "https://example.com/suspect-image.png"
}
```

**Audio detection result:**
```json
{ "has_watermark": true, "confidence": 0.95 }
```

**Image/Video detection result:**
```json
{ "has_watermark": true }
```

---

## Phase 5: Identity — Speaker Verification (Beta)

Create voice identity profiles and match incoming audio against them.

> **Beta feature** — requires joining the preview program. Inform the user if they encounter access errors.

### Create an Identity Profile

```
POST /identity
Content-Type: application/json
Authorization: Bearer <API_KEY>

{
  "audio_url": "https://example.com/known-speaker.wav",
  "name": "Jane Doe"
}
```

### Search Against Known Identities

```
POST /identity/search
Content-Type: application/json
Authorization: Bearer <API_KEY>

{
  "audio_url": "https://example.com/unknown-speaker.wav",
  "top_k": 5
}
```

**Response:**
```json
{
  "success": true,
  "item": [
    { "uuid": "...", "name": "Jane Doe", "confidence": 0.92, "distance": 0.08 }
  ]
}
```

Lower `distance` = closer match. Higher `confidence` = stronger match.

---

## Phase 6: Text Detection

Detect whether text content is AI-generated or human-written.

> **Beta feature** — requires the `detect_beta_user` role or a billing plan that includes the `dfd_text` product.

### Submit a Text Detection

```
POST /text_detect
Content-Type: application/json
Authorization: Bearer <API_KEY>
```

Add the `Prefer: wait` header for a synchronous (blocking) response. Without it, the job runs asynchronously — poll or use a callback.

**Parameters:**

| Parameter      | Type    | Required | Description                                              |
|----------------|---------|----------|----------------------------------------------------------|
| `text`         | string  | Yes      | Text to analyze (max 100,000 characters)                 |
| `thinking`     | string  | No       | Always use `"low"` (default)                             |
| `threshold`    | float   | No       | Decision threshold 0.0–1.0 (default: 0.5)               |
| `callback_url` | string  | No       | Webhook URL for async completion notification             |
| `privacy_mode` | boolean | No       | If true, text content is not stored after analysis        |

**Response:**
```json
{
  "success": true,
  "item": {
    "uuid": "abc-123",
    "status": "completed",
    "prediction": "ai",
    "confidence": 0.91,
    "text_content": "This is some text to analyze.",
    "privacy_mode": false,
    "created_at": "...",
    "updated_at": "..."
  }
}
```

- `prediction`: `"ai"` or `"human"` — the verdict
- `confidence`: 0.0–1.0, higher = more confident in the prediction
- `status`: `"processing"`, `"completed"`, or `"failed"`

### Poll for Results

If you did not use `Prefer: wait`, poll until `status` is `"completed"` or `"failed"`:

```
GET /text_detect/{uuid}
Authorization: Bearer <API_KEY>
```

### List Text Detections

```
GET /text_detect
Authorization: Bearer <API_KEY>
```

Returns paginated text detections for the team.

### Callback

If `callback_url` was provided, a `POST` is sent on completion:
```json
{ "success": true, "item": { ... } }
```
On failure:
```json
{ "success": false, "item": { ... }, "error": "Error message here" }
```

---

## Recommended Workflows

### Full Media Forensics (Most Thorough)

For a comprehensive analysis, combine all capabilities:

1. Submit detection with all flags enabled:
   ```json
   {
     "url": "https://example.com/suspect.mp4",
     "visualize": true,
     "intelligence": true,
     "audio_source_tracing": true,
     "use_reverse_search": true
   }
   ```
2. Poll until `status: "completed"`
3. Read `metrics` / `image_metrics` / `video_metrics` for the verdict
4. Read `intelligence.description` for structured media analysis
5. If audio labeled `"fake"`, check `audio_source_tracing.label` for the source platform
6. Ask follow-up questions via Detect Intelligence if anything needs clarification
7. Check for watermarks via `POST /watermark/detect` if provenance is relevant

### Quick Authenticity Check (Fastest)

For a fast pass/fail:

1. Submit minimal detection: `{ "url": "..." }`
2. Poll until complete
3. Check `label` and `aggregated_score` (audio) or `label` and `score` (image/video)
4. Report result with score context

### Provenance Pipeline (Content Creators)

For creators who want to prove their content is authentic:

1. Apply watermark to original content: `POST /watermark/apply`
2. Distribute watermarked media
3. Later, verify provenance: `POST /watermark/detect` against any copy

---

## Red Flags — Stop and Reassess

- **Declaring authenticity without a detection result** — Never say media is real or fake based on visual/auditory inspection alone
- **Ignoring the score and reporting only the label** — A `"fake"` label with score 0.51 means something very different from score 0.95
- **Submitting local file paths to the API** — The API requires publicly accessible HTTPS URLs (does not apply to text detection)
- **Sending text longer than 100,000 characters to text detection** — Split into chunks or inform the user of the limit
- **Polling too aggressively** — Start at 2s intervals, back off exponentially; do not loop at <1s
- **Asking Detect Intelligence questions before detection completes** — Results in 422 error
- **Expecting source tracing on "real" audio** — Source tracing only runs on audio labeled `"fake"`
- **Treating beta features (Identity) as production-ready** — Warn users about beta status
- **Ignoring `zero_retention_mode` for sensitive media** — Always suggest this flag when the user indicates the media is sensitive or private
- **Making multiple separate API calls when flags can combine** — Use `intelligence: true` and `audio_source_tracing: true` on the detection call instead of separate requests

## Response Presentation Guidelines

When presenting results to users:

1. **Lead with the verdict** — "The detection indicates this audio is likely AI-generated (score: 0.87)"
2. **Provide score context** — Use the score interpretation table above
3. **Mention limitations** — Detection is probabilistic, not absolute proof
4. **Include actionable next steps** — Suggest intelligence queries, source tracing, or watermark checks as appropriate
5. **For inconclusive results (0.3–0.5)** — Explicitly state the result is inconclusive and recommend additional analysis with different parameters or manual review
6. **Never present detection as legal evidence** — Detection results are analytical tools, not forensic certifications

## Error Handling

| Error     | Cause                                    | Resolution                                      |
|-----------|------------------------------------------|--------------------------------------------------|
| 400       | Invalid request body or missing `url`    | Check required parameters                        |
| 401       | Invalid or missing API key               | Verify `RESEMBLE_API_KEY`                        |
| 404       | Detection UUID not found                 | Verify the UUID from the creation response       |
| 422       | Detection not completed (for Intelligence) | Wait for detection to reach `completed` status  |
| 429       | Rate limited                             | Back off and retry with exponential delay         |
| 500       | Server error                             | Retry once, then report to user                  |

## Privacy & Compliance Notes

- **Zero retention mode**: Set `zero_retention_mode: true` to auto-delete media after analysis. The URL is redacted and `media_deleted` is set to true post-completion.
- **Text privacy mode**: Set `privacy_mode: true` on text detection to prevent text content from being stored after analysis.
- **Data handling**: Media URLs and text content are stored by default. For GDPR/compliance-sensitive workflows, enable zero retention (media) or privacy mode (text).
- **Callback security**: If using `callback_url`, ensure the endpoint is HTTPS and authenticated on the receiving end.

---

**Source:** [`davepoon/buildwithclaude`](https://github.com/davepoon/buildwithclaude) → `plugins/all-skills/skills/resemble-detect/SKILL.md`

**Also appears in:** `Prat011/awesome-llm-skills/resemble-detect/SKILL.md`