NAS-AudioDeepfake 🎯

Audio Deepfake Detection for Modern TTS Systems
Leveraging PC-DARTS architecture with custom training pipeline for multilingual deployment and SOTA TTS robustness

🚀 Key Innovations

• 🌍 Modern TTS Architecture: PC-DARTS framework adapted for 12+ state-of-the-art TTS systems
• 🌏 Multilingual Training Pipeline: Custom implementation supporting Chinese (50%) and English (30%) with 10+ language coverage
• ⚡ Production Architecture: Real-time PC-based deployment with optimized neural architecture search
• 🔬 Domain Adaptation Framework: Systematic architecture application for contemporary deepfake challenges

🎯 Problem Statement & Motivation

The Challenge

Existing anti-spoofing models fail in real-world scenarios due to:

• Temporal Gap: ASVspoof dataset is outdated, while TTS technology has advanced dramatically
• Language Limitation: Most models trained on English-only datasets fail on Chinese and other languages
• TTS Evolution: Modern systems like VALL-E, Bark, and MMS generate highly realistic speech
• Deployment Gap: Research models not optimized for real-time PC deployment

Business Impact

• Voice Authentication Security: Banking and finance systems vulnerable to modern TTS attacks
• Real-time Monitoring: Need for continuous user voice verification in production systems
• Multilingual Markets: Chinese market requires robust Chinese language support

📊 Dataset & Methodology Innovation

Custom Multilingual TTS Dataset

Component	Details	Rationale
Modern TTS Models	12+ SOTA systems	Reflect current threat landscape
Language Distribution	Chinese (50%), English (30%), Others (20%)	Target market requirements
Bonafide Sources	AISHELL + ASVspoof2019 eval	Professional recording quality
Total Scale	20k samples across 12 TTS systems	Systematic threat modeling dataset

🔬 Technical Innovation

1. Neural Architecture Search Implementation

• PC-DARTS Framework: Implemented differentiable architecture search for audio domain
• Architecture Adaptation: Custom cell design optimized for temporal audio features
• Search Space Optimization: Tailored for modern TTS detection requirements

2. Cross-lingual Training Framework

• Custom Pipeline: Multilingual training system built from ground up
• Feature Engineering: Language-agnostic audio representations
• Domain Transfer: Architecture application across diverse linguistic contexts

3. Production-First Architecture Design

• Real-time Constraints: Neural architecture optimized for <50ms inference
• Resource Efficiency: Implementation designed for consumer PC hardware
• Scalable Framework: Modular architecture supporting various deployment scenarios

4. Systematic Experimental Framework

• Ablation Studies: Comprehensive analysis of architecture components
• Hyperparameter Optimization: Optuna-based automated tuning for multilingual training
• Performance Engineering: End-to-end optimization from architecture to deployment

🏗️ System Architecture

📁 Project Structure
├── 🧠 models/              # PC-DARTS neural architecture
├── 📊 ASVDataloader/       # Custom audio data pipeline  
├── 🔧 experiments/         # Systematic experiment framework
│   ├── baseline/           # Original PC-DARTS implementation
│   ├── augmentation_study/ # Data augmentation research
│   ├── loss_optimization/  # Advanced loss functions
│   └── evaluation/         # Performance assessment
├── 🌐 web_demo/           # Production web interface
├── ⚡ inference/          # Optimized prediction pipeline
└── 📈 results/            # Experiment tracking & analysis

📈 Experimental Results

Model Performance Comparison

Model Configuration	ASVspoof2019 EER	Custom Dataset EER	Chinese Performance	Real-time Capable
Original PC-DARTS	[BASELINE]	[POOR]	Not Supported	❌
+ Custom Training Pipeline (v1)	[PLACEHOLDER]	[IMPROVED]	[PLACEHOLDER]	✅
+ Data Augmentation Strategy	7.95%	[PLACEHOLDER]	[PLACEHOLDER]	✅
+ Loss Engineering	7.00%	[BEST]	[BEST]	✅

Key Architecture Insights

• Domain Gap Challenge: Original architecture required substantial adaptation for modern TTS
• Language Generalization: Neural architecture search principles effectively transfer across languages
• Training Pipeline Impact: Custom implementation critical for contemporary threat detection
• Production Viability: Architecture maintains efficiency while improving robustness

Ablation Study Results

Component	Contribution	Key Insight
Modern TTS Training Data	[MAJOR]	Essential for contemporary threat detection
Multilingual Fine-tuning	[SIGNIFICANT]	Enables cross-language generalization
Loss Function Engineering	13.4% relative improvement	Label smoothing reduces overconfidence
Production Optimization	<50ms latency	Real-time deployment feasible

🚀 Quick Start

Installation & Setup

git clone https://github.com/kaylals/NAS-AudioDeepfake.git
cd NAS-AudioDeepfake
pip install -r requirements.txt

Training Pipeline

# Baseline training
python experiments/baseline/train_model.py --config configs/baseline.yaml

# Optimized training with label smoothing
python experiments/loss_optimization/finetune_v2.py --config configs/label_smoothing.yaml

# Hyperparameter optimization
python experiments/optimization/finetune_optuna.py

Inference & Demo

# Single model prediction
python inference/detect.py --model finetune_models/best_model.pth --audio test.wav

# Web demo
cd web_demo && python app.py
# Access at http://localhost:5000

🎯 Business Impact & Real-World Applications

Target Applications

• Financial Services: Real-time voice authentication for banking and payments
• Enterprise Security: Employee voice verification for remote work environments
• Content Moderation: Automated detection of synthetic audio in social media
• Legal Evidence: Forensic analysis of audio authenticity in court proceedings

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🔗 Connect with me: LinkedIn | 📧 Email: shuoliu10@gmail.com

This project demonstrates expertise in deep learning research, systematic experimentation, and production ML system design.