deepscan - Deepfake Detection & Media Authentication

Detect deepfakes and verify media authenticity with forensic analysis.

Advanced deepfake detection and media authentication tool for verifying digital content.

🚀 Features

• Deepfake Detection: Identify AI-generated fake media
• Media Authentication: Verify media authenticity
• Forensic Analysis: Comprehensive forensic examination
• Multiple Analysis Methods: Frequency, texture, landmark analysis
• Metadata Verification: Validate EXIF and other metadata
• Blockchain Verification: Verify media via blockchain registry

📦 Installation

Build from Source

git clone https://github.com/hallucinaut/deepscan.git
cd deepscan
go build -o deepscan ./cmd/deepscan
sudo mv deepscan /usr/local/bin/

Install via Go

go install github.com/hallucinaut/deepscan/cmd/deepscan@latest

🎯 Usage

Analyze Media

# Analyze media for deepfakes
deepscan analyze /path/to/image.jpg

Verify Authenticity

# Verify media authenticity
deepscan verify /path/to/image.jpg

Forensic Analysis

# Perform forensic analysis
deepscan forensic /path/to/image.jpg

Compare Media

# Compare two media files
deepscan compare image1.jpg image2.jpg

Generate Report

# Generate analysis report
deepscan report

Programmatic Usage

package main

import (
    "fmt"
    "github.com/hallucinaut/deepscan/pkg/analyze"
    "github.com/hallucinaut/deepscan/pkg/verify"
    "github.com/hallucinaut/deepscan/pkg/forensic"
)

func main() {
    // Create analyzer
    analyzer := analyze.NewMediaAnalyzer()
    
    // Analyze file
    result, err := analyzer.AnalyzeFile("/path/to/image.jpg")
    if err != nil {
        panic(err)
    }
    
    fmt.Printf("Is Deepfake: %v\n", result.IsDeepfake)
    fmt.Printf("Confidence: %.2f%%\n", result.Confidence*100)
    
    // Verify media
    verifyResult, err := verify.VerifyFile("/path/to/image.jpg")
    if err != nil {
        panic(err)
    }
    
    fmt.Printf("Verification: %v\n", verifyResult.IsValid)
    
    // Forensic analysis
    forensicAnalyzer := forensic.NewForensicAnalyzer()
    forensicResult, err := forensicAnalyzer.AnalyzeFile("/path/to/image.jpg")
    if err != nil {
        panic(err)
    }
    
    fmt.Printf("Authentic: %v\n", forensicResult.IsAuthentic)
}

📚 Detection Methods

Frequency Analysis

• Analyze frequency domain artifacts
• Detect unnatural frequency patterns
• Identify AI-generated frequencies

Texture Analysis

• Examine texture consistency
• Detect synthetic textures
• Identify smoothing artifacts

Face Landmark Analysis

• Analyze facial feature consistency
• Detect landmark irregularities
• Identify manipulation artifacts

Temporal Consistency

• Check frame-to-frame consistency
• Detect temporal anomalies
• Identify synthetic motion

🧪 Analysis Methods

Method	Description
Frequency	Analyze frequency domain
Texture	Analyze texture patterns
Landmark	Analyze facial landmarks
Metadata	Verify EXIF metadata
Forensic	Comprehensive forensic analysis

🏗️ Architecture

deepscan/
├── cmd/
│   └── deepscan/
│       └── main.go          # CLI entry point
├── pkg/
│   ├── analyze/
│   │   ├── analyze.go      # Media analysis
│   │   └── analyze_test.go # Unit tests
│   ├── verify/
│   │   ├── verify.go       # Media verification
│   │   └── verify_test.go  # Unit tests
│   └── forensic/
│       ├── forensic.go     # Forensic analysis
│       └── forensic_test.go # Unit tests
└── README.md

🧪 Testing

# Run all tests
go test ./...

# Run with coverage
go test -cover ./...

# Run specific test
go test -v ./pkg/analyze -run TestAnalyzeFile

📋 Example Output

$ deepscan analyze /path/to/image.jpg

Media Analysis for Deepfake Detection
======================================

Analysis Result:
  File: /path/to/image.jpg
  Format: JPEG
  Dimensions: 1920x1080
  Size: 245678 bytes
  Hash: a1b2c3d4e5f67890...
  Is Deepfake: false
  Deepfake Score: 5.00%
  Confidence: 95.00%
  Status: Valid

🔒 Security Use Cases

• Deepfake Detection: Detect AI-generated fake media
• Media Authentication: Verify media authenticity
• Forensic Investigation: Investigate media authenticity
• Content Verification: Verify social media content
• Legal Evidence: Authenticate digital evidence

🛡️ Best Practices

1. Use multiple methods: Combine detection methods
2. Verify metadata: Check EXIF and other metadata
3. Compare with originals: If available
4. Check blockchain: Verify via blockchain registry
5. Maintain chain of custody: For legal evidence
6. Regular updates: Keep detection models updated

📄 License

MIT License

🙏 Acknowledgments

• Deepfake research community
• Media forensics researchers
• AI security community

🔗 Resources

• Deepfake Detection Challenge
• FakeCatcher
• Forensic Image Analysis

---

build with GPU by hallucinaut