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 (set as RESEMBLE_API_KEY)
• 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. (Exception: POST /text_detect accepts text content inline.)

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.

Full API Reference

Detailed request/response schemas for every endpoint are in references/api-reference.md. Consult it before making any API call to verify exact parameter names and response shapes. The sections below cover decision-making; the reference covers exact field formats.

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Phase 1: Deepfake Detection

The core capability. Submit audio, image, or video for AI-generated content analysis via POST /detect.

Key flags to consider:

• visualize: true — generate heatmap/visualization artifacts
• intelligence: true — run multimodal intelligence alongside detection (saves a round-trip)
• audio_source_tracing: true — identify which AI platform synthesized fake audio (only fires on "fake" audio)
• use_reverse_search: true — enable reverse image search (image only)
• zero_retention_mode: true — auto-delete media after analysis (for sensitive content)

Detection is asynchronous. Poll GET /detect/{uuid} at 2s → 5s → 10s intervals until status is "completed" or "failed". Most complete in 10–60 seconds.

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

Reading Results

• Audio — verdict in metrics — use label and aggregated_score
• Image — verdict in image_metrics — use label and score; ifl has an Invisible Frequency Layer heatmap
• Video — verdict in video_metrics — hierarchical tree of frame/segment results; video-with-audio returns both metrics and video_metrics

See references/api-reference.md for full response schemas.

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."

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Phase 2: Intelligence — Media Analysis

Rich structured insights about media: speaker info, emotion, transcription, translation, misinformation, abnormalities.

Two ways to run Intelligence:

1. Combined with detection — add intelligence: true to POST /detect (preferred; one call)
2. Standalone — POST /intelligence with a URL (when you only need analysis, not a deepfake verdict)

Audio/video structured fields include: speaker_info, language, dialect, emotion, speaking_style, context, message, abnormalities, transcription, translation, misinformation.

Image structured fields include: scene_description, subjects, authenticity_analysis, context_and_setting, abnormalities, misinformation.

Detect Intelligence — Ask Questions About Results

After a detection completes, ask natural-language questions via POST /detects/{detect_uuid}/intelligence with { "query": "..." }. Returns a question UUID — poll GET /detects/{detect_uuid}/intelligence/{question_uuid} until completed.

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?"

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

See references/api-reference.md for full parameters.

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Phase 3: Audio Source Tracing

When audio is labeled "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.

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

Important: Source tracing only runs on audio labeled "fake". Real audio produces no source tracing result.

Standalone queries: GET /audio_source_tracings and GET /audio_source_tracings/{uuid}.

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Phase 4: Watermarking

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

• Apply: POST /watermark/apply with url, optional strength (0.0–1.0), optional custom_message. Add Prefer: wait for synchronous response, or poll GET /watermark/apply/{uuid}/result. Response includes watermarked_media URL.
• Detect: POST /watermark/detect with url. Audio returns { has_watermark, confidence }; image/video returns { has_watermark }.

See references/api-reference.md for exact parameter rules.

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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 profile: POST /identity with { audio_url, name }
• Search: POST /identity/search with { audio_url, top_k }

Response returns ranked matches with confidence (higher = stronger) and distance (lower = closer match).

See references/api-reference.md for full schemas.

---

Phase 6: Text Detection

Detect whether text content is AI-generated or human-written via POST /text_detect.

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

Key parameters:

• text (required, max 100,000 chars)
• threshold (default 0.5)
• privacy_mode: true — text content not stored after analysis
• callback_url — async notification webhook

Add Prefer: wait for synchronous response, or poll GET /text_detect/{uuid}. Response includes prediction ("ai" or "human") and confidence (0.0–1.0).

See references/api-reference.md for full schema and callback format.

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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)

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)

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

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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, Text Detection) 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.