🎵 Audio Deepfake Detection Model
Overview
Audio deepfakes have become increasingly sophisticated, utilizing powerful Text-to-Speech (TTS) synthesizers, voice conversion networks, and neural vocoders to create convincing voice clones and forge entire soundscapes. These attacks can range from simple voice modifications to complete voice identity theft.
Our Audio Detection Model leverages advanced spectral analysis and deep neural networks to identify even the most subtle audio manipulations, providing robust protection against voice cloning and synthetic speech attacks.
🌟 Key Features
| Feature | Description |
|---|---|
| 🗣️ Voice Clone Detection | Identifies AI-generated voices and sophisticated voice conversion |
| ⏱️ Short Segment Analysis | Effective detection on segments as short as 4 seconds |
| 🎚️ Manipulation Detection | Detects pitch, tempo, and other audio modifications |
| 🔊 Multi-Channel Support | Analyzes both mono and stereo audio streams |
| 🎯 High Precision | Advanced spectral analysis for accurate detection |
⚙️ How It Works
Our audio detection system employs a sophisticated sliding-window analysis approach:
1. 🔄 Segmentation Process
- Audio is automatically segmented into 4-second windows
- 50% overlap between windows ensures comprehensive coverage
- Maintains temporal context while enabling detailed analysis
2. 🧠 Spectral Analysis
- Deep neural networks analyze spectral discrepancies
- Identifies synthetic artifacts in frequency domain
- Detects unnatural harmonics and formant patterns
3. 📊 Confidence Scoring
- Each window receives a confidence score (0 to 1)
- Higher scores indicate greater likelihood of manipulation
- Aggregated results provide overall assessment
📥 Input Specifications
Supported Formats
🎥 Video Audio Extraction
- Source: Audio tracks from video files
- Channels: Mono and stereo support
- Processing: Automatic audio stream extraction
🎵 Direct Audio Input
- Format: WAV (primary support)
- Quality: Standard to high-quality audio
- Duration: No minimum length requirement