> ## Documentation Index
> Fetch the complete documentation index at: https://docs.tuteliq.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Synthetic Content Detection
> Multi-signal forensic detection of AI-generated text, images, audio, and video — deepfakes, voice cloning, synthetic identities, and AI-enhanced child exploitation content
Tuteliq detects AI-generated and synthetic content across all four modalities — text, image, audio, and video. Each endpoint classifies content using the standardized taxonomy from the [Child Protection Blueprint](https://protectingchildrenonline.org): `confirmed_synthetic`, `suspected_synthetic`, `unknown`, or `confirmed_authentic`.
For images and video, Tuteliq goes far beyond a single model call. The **multi-signal forensic pipeline** runs up to 6 independent analysis engines in parallel — vision forensics, EXIF metadata, pixel statistics, C2PA Content Credentials, watermark detection, and perceptual hashing — then aggregates them into a weighted ensemble assessment. Any single engine can fail without degrading the result.
Vision AI, EXIF metadata, pixel statistics, C2PA Content Credentials, frequency-domain watermark analysis, and perceptual hash matching — all in parallel.
Frame-by-frame face identity tracking, landmark stability analysis, and audio-visual lip-sync correlation to catch deepfakes that single-frame analysis misses.
Mel spectrogram analysis via vision AI plus quantitative audio statistics — dynamic range, silence ratio, flat factor — to detect synthetic speech beyond transcript analysis.
## Classification Levels
| Classification | Description |
| --------------------- | ---------------------------------------------- |
| `confirmed_synthetic` | High confidence AI-generated content detected |
| `suspected_synthetic` | Moderate indicators of synthetic content |
| `unknown` | Insufficient data to determine authenticity |
| `confirmed_authentic` | High confidence genuine, human-created content |
## Category Taxonomy
| Tag | Description |
| ------------------------- | --------------------------------------------------------- |
| `AI_GENERATED_TEXT` | LLM-generated text (ChatGPT, Claude, etc.) |
| `AI_GENERATED_IMAGE` | AI-generated image (Midjourney, DALL-E, Stable Diffusion) |
| `AI_GENERATED_AUDIO` | Voice cloning, text-to-speech synthesis |
| `AI_GENERATED_VIDEO` | Fully AI-generated video content |
| `AI_MANIPULATED_MEDIA` | Deepfakes, face swaps, manipulated media |
| `SYNTHETIC_IDENTITY` | Fake identity created using AI tools |
| `SYNTHETIC_CSAM` | AI-generated child sexual abuse material |
| `SYNTHETIC_IMPERSONATION` | AI-generated impersonation of a real person |
| `AI_ENHANCED_GROOMING` | AI-assisted grooming scripts |
| `AI_ENHANCED_SEXTORTION` | AI-assisted sextortion content |
`SYNTHETIC_CSAM` always escalates to severity 1.0, level `critical`, and recommended action `immediate_intervention` regardless of confidence score.
***
## Text Detection
Analyzes text for AI-generated content indicators — LLM-generated text, synthetic identities, AI-enhanced grooming scripts, and more.
```
POST /api/v1/safety/synthetic-content
Content-Type: application/json
```
### Request
```json theme={"dark"}
{
"text": "In conclusion, it is important to note that there are several key factors to consider when evaluating the implications of this complex issue.",
"context": {
"language": "en",
"age_group": "13-15",
"platform": "discord"
},
"external_id": "msg_123",
"customer_id": "user_456"
}
```
### Response
```json theme={"dark"}
{
"endpoint": "synthetic-content",
"detected": true,
"severity": 0.8,
"level": "high",
"classification": "suspected_synthetic",
"confidence": 0.9,
"risk_score": 0.7,
"categories": [
{ "tag": "AI_GENERATED_TEXT", "label": "AI-generated text", "confidence": 0.9 }
],
"evidence": [
{ "text": "Uniform hedging pattern", "tactic": "STATISTICAL_LANGUAGE", "weight": 0.6 }
],
"age_calibration": { "applied": true, "age_group": "13-15", "multiplier": 1.3 },
"recommended_action": "flag_for_review",
"rationale": "Text shows uniform style and formulaic structure typical of LLM output.",
"processing_time_ms": 2225,
"language": "en",
"language_status": "stable",
"credits_used": 5
}
```
**Credits:** 5
***
## Image Detection
Analyzes uploaded images for AI-generation artifacts using a **6-signal forensic pipeline** — running vision analysis, EXIF metadata extraction, pixel statistics, C2PA Content Credentials, watermark detection, and perceptual hashing in parallel.
```
POST /api/v1/safety/synthetic-content/image
Content-Type: multipart/form-data
```
### How It Works
Send an image as `multipart/form-data`. Supported formats: **JPEG, PNG, WebP, GIF**. Max file size: **10MB**.
Six independent analysis engines run in parallel, each fault-isolated so failures in one don't affect others:
1. **Vision AI** — Forensic prompt inspects pixel-level artifacts (face consistency, skin texture, hand anomalies, background coherence, lighting mismatches)
2. **EXIF Metadata** — Checks for AI generator signatures in EXIF tags, XMP data, and PNG text chunks. Flags suspicious absence of camera metadata.
3. **Pixel Statistics** — Shannon entropy, edge density (Laplacian convolution), and channel uniformity analysis
4. **C2PA Content Credentials** — Detects and validates C2PA manifests from DALL-E, Adobe Firefly, Google Imagen, and other tools
5. **Watermark Detection** — Frequency-domain analysis for invisible watermarks (SynthID, Stable Diffusion DWT/DCT)
6. **Perceptual Hashing** — DCT-based pHash compared against a database of known synthetic content via Hamming distance
All signals are combined into a weighted ensemble: vision (30%), metadata (15%), pixel statistics (15%), C2PA provenance (15%), watermarks (10%), perceptual hash (15%). The aggregated forensic summary is fed to the classifier.
The classifier produces a final verdict. Two signals can override the classifier with definitive results:
* **C2PA declares AI generation** → forced to `confirmed_synthetic` with confidence ≥ 0.95
* **Perceptual hash matches known synthetic** → forced to `confirmed_synthetic` with confidence ≥ 0.90
### Request Fields
| Field | Type | Required | Description |
| ------------- | ------ | -------- | -------------------------------------------- |
| `file` | File | Yes | Image file (JPEG, PNG, WebP, GIF). Max 10MB. |
| `age_group` | string | No | Age group context (e.g., `"13-15"`) |
| `language` | string | No | ISO language code |
| `platform` | string | No | Platform context |
| `external_id` | string | No | Your internal reference ID |
| `customer_id` | string | No | Your customer/user ID |
### Response
```json theme={"dark"}
{
"endpoint": "synthetic-content",
"detected": true,
"severity": 0.9,
"level": "high",
"classification": "confirmed_synthetic",
"confidence": 0.95,
"risk_score": 0.85,
"categories": [
{ "tag": "AI_GENERATED_IMAGE", "label": "AI-generated image", "confidence": 0.95 }
],
"evidence": [
{ "text": "Uniform skin texture and symmetrical facial features", "tactic": "AI_GENERATION_ARTIFACT_DETECTION", "weight": 0.9 }
],
"recommended_action": "flag_for_review",
"rationale": "Multi-signal forensic analysis: vision AI detected artifacts, EXIF metadata lacks camera model, C2PA manifest confirms AI generation by DALL-E 3.",
"input_type": "image",
"vision": {
"is_likely_synthetic": true,
"synthetic_confidence": 0.95,
"artifacts": [
"unnaturally uniform skin texture on cheeks with complete absence of pores",
"perfectly symmetrical facial features",
"blurred background with smooth transitions characteristic of AI-generated depth of field"
],
"face_analysis": "Unnaturally smooth skin with no visible pores, perfectly symmetrical features.",
"overall_assessment": "Image exhibits several indicators of AI generation."
},
"metadata_analysis": {
"format": "png",
"dimensions": { "width": 1024, "height": 1024 },
"has_exif": false,
"has_camera": false,
"has_gps": false,
"ai_generator_detected": true,
"ai_generator": "DALL-E",
"suspicious_absence": true
},
"provenance": {
"has_c2pa": true,
"claim_generator": "DALL-E 3",
"is_ai_generated": true,
"ai_tool": "DALL-E 3"
},
"forensic_signals": {
"signal_count": 8,
"sources": [
{ "name": "vision", "signal_count": 3, "confidence_boost": 0.285 },
{ "name": "metadata", "signal_count": 2, "confidence_boost": 0.2 },
{ "name": "provenance", "signal_count": 1, "confidence_boost": 0.5 }
],
"combined_confidence_boost": 0.35
},
"perceptual_hash": "a3b2c1d4e5f60718",
"processing_time_ms": 3100,
"credits_used": 8
}
```
**Credits:** 8 (5 base + 3 vision)
### Forensic Signal Sources
| Signal | Weight | What It Checks |
| -------------------- | ------ | --------------------------------------------------------------------------------------------------------------------------------------------- |
| **Vision AI** | 30% | Face consistency, skin texture, hand anomalies, background coherence, lighting, hair rendering, text/symbols, resolution, AI model signatures |
| **EXIF Metadata** | 15% | Camera model, GPS, AI generator names in EXIF/XMP, PNG tEXt chunks (Stable Diffusion parameters), suspicious absence of camera data |
| **Pixel Statistics** | 15% | Shannon entropy, Laplacian edge density, channel uniformity — GAN images have distinctive statistical signatures |
| **C2PA Provenance** | 15% | Content Credentials manifests from DALL-E, Firefly, Imagen. **Definitive** when present — overrides classifier. |
| **Watermark** | 10% | High-frequency energy analysis, periodic pattern detection at known watermark frequencies, LSB distribution, corner entropy |
| **Perceptual Hash** | 15% | DCT-based 64-bit pHash compared against known-synthetic database. Hamming distance ≤ 10 = match. **Definitive** when matched. |
### New Response Fields
| Field | Type | Description |
| ----------------------- | ------ | ------------------------------------------------------------------------------------------------------ |
| `metadata_analysis` | object | EXIF/metadata extraction results — format, dimensions, camera presence, GPS, AI generator detection |
| `provenance` | object | C2PA Content Credentials — present only when a C2PA manifest is found |
| `forensic_signals` | object | Multi-signal ensemble summary — signal counts per source and combined confidence boost |
| `perceptual_hash` | string | 64-bit DCT-based perceptual hash of the image |
| `known_synthetic_match` | object | Present only when the perceptual hash matches a known synthetic image — includes distance and category |
***
## Audio Detection
Analyzes uploaded audio using **dual-signal forensics**: transcript-based text analysis plus spectral analysis — mel spectrogram via vision AI and quantitative audio statistics for synthetic speech indicators.
```
POST /api/v1/safety/synthetic-content/audio
Content-Type: multipart/form-data
```
### How It Works
Send an audio file as `multipart/form-data`. Supported formats: **MP3, WAV, M4A, OGG, FLAC, WebM**. Max file size: **25MB**.
Two analysis tracks run simultaneously:
1. **Transcription** — Audio is transcribed using Whisper (EU-hosted, GDPR-compliant)
2. **Spectral Analysis** — FFmpeg generates a mel spectrogram image and extracts audio statistics (RMS, dynamic range, silence ratio, flat factor, DC offset)
The mel spectrogram is analyzed by a dedicated forensic prompt checking for:
* Frequency band uniformity (TTS hallmark)
* Harmonic structure anomalies
* Missing breath noise and background ambience
* Onset/offset patterns, formant transitions
* Pitch contour regularity, aliasing artifacts
Transcript, spectral signals, and spectrogram analysis are combined for final classification. Even audio with no speech can be flagged if spectral analysis detects synthetic patterns.
### Request Fields
| Field | Type | Required | Description |
| ------------- | ------ | -------- | ------------------------------------------------------ |
| `file` | File | Yes | Audio file (MP3, WAV, M4A, OGG, FLAC, WebM). Max 25MB. |
| `age_group` | string | No | Age group context |
| `language` | string | No | ISO language code |
| `platform` | string | No | Platform context |
| `external_id` | string | No | Your internal reference ID |
| `customer_id` | string | No | Your customer/user ID |
### Response
```json theme={"dark"}
{
"endpoint": "synthetic-content",
"detected": true,
"severity": 0.6,
"level": "medium",
"classification": "suspected_synthetic",
"confidence": 0.7,
"risk_score": 0.5,
"categories": [
{ "tag": "AI_GENERATED_AUDIO", "label": "AI-Generated Audio", "confidence": 0.7 }
],
"recommended_action": "flag_for_review",
"rationale": "Spectral analysis shows unnaturally uniform frequency bands and missing breath noise. Transcript structure is consistent with TTS output.",
"input_type": "audio",
"transcription": {
"text": "Hello, I am calling about your account.",
"language": "en",
"duration": 3.5,
"segments": [
{ "start": 0, "end": 3.5, "text": "Hello, I am calling about your account." }
]
},
"audio_stats": {
"rms_mean": -18.5,
"rms_peak": -6.2,
"dynamic_range": 12.3,
"silence_ratio": 0.05,
"flat_factor": 0.002,
"dc_offset": 0.0001
},
"spectral_signals": [
"low_dynamic_range: 12.3 dB suggests compressed/synthetic audio",
"low_silence_ratio: 0.05 — natural speech typically has more pauses"
],
"processing_time_ms": 4200,
"language": "en",
"language_status": "stable",
"credits_used": 10
}
```
**Credits:** 7 (5 base + 2 transcription) or 10 (+ 3 for spectrogram vision analysis)
### New Response Fields
| Field | Type | Description |
| ------------------ | ------ | --------------------------------------------------------------------------------------------------- |
| `audio_stats` | object | Quantitative audio statistics — RMS mean/peak, dynamic range, silence ratio, flat factor, DC offset |
| `spectral_signals` | array | Human-readable spectral analysis indicators (e.g., low dynamic range, frequency uniformity) |
If the audio contains no intelligible speech but spectral analysis detects synthetic patterns, the endpoint returns `classification: "suspected_synthetic"` with spectral signals. If neither speech nor spectral anomalies are found, it returns `classification: "unknown"`.
***
## Video Detection
Analyzes uploaded video with a **multi-layer forensic pipeline** — per-frame vision analysis, temporal face consistency tracking, audio-visual lip-sync correlation, spectral audio forensics, and transcription.
```
POST /api/v1/safety/synthetic-content/video
Content-Type: multipart/form-data
```
### How It Works
Send a video file as `multipart/form-data`. Supported formats: **MP4, WebM, QuickTime, AVI**. Max file size: **100MB**.
Frames are extracted at even intervals using FFmpeg (default: 6 frames, max: 20). Audio is extracted as a separate track in parallel.
Five analysis tracks run simultaneously via fault-isolated `Promise.allSettled`:
1. **Per-Frame Vision** — Each frame analyzed for AI artifacts (face consistency, skin texture, background coherence) in concurrent batches of 3
2. **Temporal Consistency** — face-api.js detects faces across all frames, computes Euclidean distance between face descriptors (real \< 0.4, deepfake > 0.6), and measures landmark stability via eye-to-nose ratio variance
3. **Lip-Sync Correlation** — Mouth openness (from 68-point face landmarks) correlated against frame-aligned audio energy (via FFmpeg). Pearson correlation > 0.5 = real speech, \< 0.3 = lip-sync deepfake
4. **Spectral Audio Analysis** — Mel spectrogram + audio statistics (same as audio endpoint)
5. **Transcription** — Whisper transcription of the audio track
All signals are aggregated via weighted ensemble and fed to the classifier. Temporal anomalies and lip-sync mismatches provide strong deepfake indicators that single-frame analysis cannot detect.
### Request Fields
| Field | Type | Required | Description |
| ------------- | ------ | -------- | -------------------------------------------------- |
| `file` | File | Yes | Video file (MP4, WebM, QuickTime, AVI). Max 100MB. |
| `max_frames` | number | No | Frames to extract (default: 6, max: 20) |
| `age_group` | string | No | Age group context |
| `language` | string | No | ISO language code |
| `platform` | string | No | Platform context |
| `external_id` | string | No | Your internal reference ID |
| `customer_id` | string | No | Your customer/user ID |
### Response
```json theme={"dark"}
{
"endpoint": "synthetic-content",
"detected": true,
"severity": 0.7,
"level": "high",
"classification": "suspected_synthetic",
"confidence": 0.8,
"risk_score": 0.65,
"categories": [
{ "tag": "AI_MANIPULATED_MEDIA", "label": "AI-Manipulated Media", "confidence": 0.8 }
],
"recommended_action": "escalate",
"rationale": "Temporal analysis detected face identity drift across 3 frame pairs. Lip-sync correlation (0.21) is well below the 0.3 threshold for authentic speech.",
"input_type": "video",
"video": {
"duration_seconds": 12.5,
"frames_analyzed": 6,
"has_audio": true
},
"temporal_consistency": {
"frames_with_faces": 6,
"total_frames": 6,
"identity_consistency_score": 0.42,
"landmark_stability_score": 0.65,
"temporal_consistency_score": 0.51,
"anomalous_frame_pairs": [
{ "frame_a": 1, "frame_b": 2, "distance": 0.72 },
{ "frame_a": 3, "frame_b": 4, "distance": 0.68 }
],
"signals": [
"identity_drift: 2 frame pairs show face identity changes (max distance: 0.720)",
"low_identity_consistency: face identity varies significantly across frames (score: 0.42)"
]
},
"lip_sync": {
"correlation": 0.21,
"has_silent_mouth_movement": false,
"has_voice_without_movement": true,
"signals": [
"poor_lip_sync: mouth-audio correlation is 0.21 (threshold: 0.3)",
"voice_without_movement: audio present in 4/6 frames without mouth movement"
]
},
"transcription": {
"text": "Speech content from the video.",
"language": "en",
"duration": 12.5
},
"audio_stats": {
"rms_mean": -20.1,
"dynamic_range": 15.8,
"silence_ratio": 0.12,
"flat_factor": 0.001
},
"processing_time_ms": 15000,
"language": "en",
"credits_used": 25
}
```
**Credits:** 5 base + 3 per frame + 2 if audio present
For example, 6 frames with audio = 5 + 18 + 2 = **25 credits**.
### New Response Fields
| Field | Type | Description |
| ---------------------- | ------ | -------------------------------------------------------------------------------------------------------------------------- |
| `temporal_consistency` | object | Face identity tracking across frames — consistency scores, landmark stability, anomalous frame pairs |
| `lip_sync` | object | Audio-visual lip-sync correlation — Pearson coefficient, silent mouth movement detection, voice-without-movement detection |
| `audio_stats` | object | Quantitative audio statistics from spectral analysis |
| `spectral_signals` | array | Spectral analysis indicators (when detected) |
### Temporal Consistency Signals
| Signal | Meaning |
| -------------------------- | -------------------------------------------------------------------------------------------- |
| `identity_drift` | Face descriptors differ significantly between consecutive frames — classic deepfake artifact |
| `low_identity_consistency` | Average face identity distance is high across all frames |
| `unstable_landmarks` | Facial geometry (eye-to-nose ratio) varies abnormally between frames |
| `consistent_face` | Face identity and geometry are stable — reduces synthetic confidence |
### Lip-Sync Signals
| Signal | Meaning |
| ------------------------ | ------------------------------------------------------------------------ |
| `poor_lip_sync` | Mouth movement and audio energy have low correlation (\< 0.3) |
| `good_lip_sync` | Strong correlation (> 0.5) between mouth and audio — authentic indicator |
| `silent_mouth_movement` | Mouth opens in > 30% of frames without corresponding audio |
| `voice_without_movement` | Audio present in > 30% of frames without mouth movement |
If the video has no audio track, the `transcription`, `lip_sync`, `audio_stats`, and `spectral_signals` fields are omitted and no transcription credits are charged.
***
## Credit Summary
| Endpoint | Path | Credits |
| -------- | --------------------------------- | ----------------------- |
| Text | `/safety/synthetic-content` | 2 |
| Image | `/safety/synthetic-content/image` | 5 |
| Audio | `/safety/synthetic-content/audio` | 4-7 |
| Video | `/safety/synthetic-content/video` | 2 + 3/frame + 2 (audio) |
## Multi-Endpoint Support
The text-based synthetic content detector is available in the [Multi-Endpoint](/api-reference/analyse) fan-out. Include `"synthetic-content"` in your endpoint list:
```json theme={"dark"}
{
"text": "content to analyze",
"endpoints": ["bullying", "grooming", "synthetic-content"]
}
```
Image, audio, and video synthetic detection are multipart-only and not available through the multi-endpoint batch.