Resemble Detect

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

Capability Decision Tree

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 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:

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:

{
  "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:

{
  "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:

{
  "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

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:

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:

{
  "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"
}

• 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:

{ "has_watermark": true, "confidence": 0.95 }

Image/Video detection result:

{ "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:

{
  "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:

Response:

{
  "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:

{ "success": true, "item": { ... } }

On failure:

{ "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:

   {
     "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

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.