Deepfake Detection

Protect your business from AI-generated fraud with comprehensive deepfake detection across images, videos, audio, and documents.

What are Deepfakes?

Deepfakes are synthetic media created or manipulated using artificial intelligence. Fraudsters use deepfakes to:

• Bypass identity verification with fake IDs
• Impersonate individuals in video calls
• Clone voices for social engineering
• Create fraudulent documents

Detection Capabilities

Image Analysis

Detect AI-generated or manipulated images:

What We Detect:

• Face swaps and morphing
• AI-generated faces
• Photo manipulation
• Document forgery
• Screenshot detection

Common Attack Vectors:

• Fake profile pictures
• Manipulated ID documents
• AI-generated selfies
• Photoshopped documents

2 Credits Per image analysis

Learn more about Image Analysis →

Video Analysis

Detect deepfake videos and pre-recorded content:

What We Detect:

• Face replacement in videos
• Video manipulation artifacts
• Pre-recorded video playback
• Screen recording detection
• Temporal inconsistencies

Common Attack Vectors:

• Fake liveness videos
• AI-generated video calls
• Replayed verification videos
• Manipulated identity videos

5 Credits Per video analysis

Learn more about Video Analysis →

Audio Analysis

Detect voice cloning and audio manipulation:

What We Detect:

• AI-generated voices
• Voice cloning
• Audio splicing
• Text-to-speech detection
• Background noise inconsistencies

Common Attack Vectors:

• Fake customer service calls
• CEO fraud (voice impersonation)
• Social engineering attacks
• Fraudulent phone verification

3 Credits Per audio analysis

Learn more about Audio Analysis →

Document Analysis

Detect tampered or AI-generated documents:

What We Detect:

• PDF manipulation
• AI-generated documents
• Copy-paste forgery
• Metadata inconsistencies
• Digital signature validation

Common Attack Vectors:

• Fake bank statements
• Forged contracts
• Manipulated invoices
• AI-generated certificates

4 Credits Per document analysis

How It Works

1. Upload Media

const formData = new FormData();
formData.append('file', imageFile);
formData.append('type', 'image');
 
const job = await fetch('/api/v4/deepfake/analyse', {
  method: 'POST',
  body: formData
});

2. AI Analysis

Our multi-layered AI models analyse:

• Pixel-level patterns: Detect manipulation artifacts
• Temporal consistency: Video frame analysis
• Audio signatures: Voice authenticity markers
• Metadata: File properties and inconsistencies
• Context: Anomaly detection

Processing time:

• Images: 2-5 seconds
• Audio: 5-10 seconds
• Documents: 5-15 seconds
• Videos: 30-120 seconds

3. Receive Results

{
  "jobId": "job_abc123",
  "status": "completed",
  "mediaType": "image",
  "result": {
    "isDeepfake": true,
    "confidence": 94,
    "manipulationType": "face_swap",
    "artifacts": [
      "Inconsistent lighting on face",
      "Blending artifacts around hairline",
      "Unnatural skin texture"
    ],
    "recommendation": "reject"
  }
}

Confidence Scores

Integration with Identity Verification

Combine deepfake detection with identity verification for maximum security:

Enhanced Verification Workflow

1. Upload ID document
   ↓
2. Deepfake scan document
   ↓
3. Take selfie
   ↓
4. Deepfake scan selfie
   ↓
5. Liveness check
   ↓
6. Face match
   ↓
7. Final decision

Automatic Triggering

Configure automatic deepfake scans:

• All identity documents
• All selfies from high-risk countries
• Random sampling (10% of verifications)
• Triggered by risk score thresholds

Use Cases

Financial Services

• Prevent fake ID fraud during account opening
• Detect manipulated financial documents
• Protect against video call impersonation

Cryptocurrency

• Combat fake KYC submissions
• Prevent social engineering attacks
• Validate high-value transaction approvals

Remote Hiring

• Verify candidate identity in video interviews
• Detect fake credentials and certificates
• Prevent resume fraud

Insurance Claims

• Detect fraudulent claim documentation
• Validate accident photos
• Prevent identity fraud in claims

Detection Technology

Image Detection Methods

• GAN Fingerprinting: Detect specific AI model signatures
• Frequency Analysis: Identify unusual spectral patterns
• Noise Pattern Analysis: Detect inconsistent image noise
• Lighting Consistency: Check for unnatural lighting
• Face Landmark Analysis: Verify natural facial geometry

Video Detection Methods

• Temporal Coherence: Frame-to-frame consistency
• Optical Flow: Motion pattern analysis
• Blinking Pattern: Natural vs. synthetic blink detection
• Head Pose: Natural movement detection
• Audio-Visual Sync: Lip-sync analysis

Audio Detection Methods

• Spectral Analysis: Frequency pattern examination
• Prosody Analysis: Natural speech rhythm
• Background Consistency: Environmental sound patterns
• Voice Biometrics: Speaker verification
• Compression Artifacts: Detect AI generation signatures

Limitations and Accuracy

Current Accuracy Rates

• Image Detection: 94% accuracy
• Video Detection: 92% accuracy
• Audio Detection: 90% accuracy
• Document Detection: 96% accuracy

Known Limitations

1. Evolving Technology: Deepfake quality improves constantly
2. Low-Quality Media: Poor source material reduces accuracy
3. Partial Deepfakes: Subtle manipulations harder to detect
4. New Techniques: Zero-day deepfake methods may evade detection

Best Practices

1. Combine with Liveness: Use liveness detection alongside deepfake scanning
2. Multi-Modal Verification: Check both image and video
3. Review Uncertain Cases: Manually verify 70-89% confidence scores
4. Stay Updated: Keep models current with latest threats
5. Document Decisions: Maintain audit trail of detections

Regulatory Landscape

Deepfake detection helps comply with emerging regulations:

• EU AI Act: Requirements for synthetic media detection
• California AB 602: Deepfake disclosure laws
• UK Online Safety Bill: Platform obligations
• Industry Standards: KYC/AML best practices

Cost Summary

Next Steps