Deepfake Audio Detection: An AI-generated Speech Detection System Based on MFCC Features

This project is an AI-generated speech detection system based on MFCC audio feature extraction and machine learning classification techniques, aiming to distinguish between AI-generated speech and human real speech. The project includes a complete workflow of data exploration, preprocessing, model training, and Streamlit deployment, providing solutions to security risks brought by AI-synthesized speech (such as fraud and identity theft), with wide application value and engineering reference significance.

With the rapid development of generative AI technology, the quality of AI-synthesized speech has reached a level of being indistinguishable from real speech, bringing serious security and ethical issues such as scam calls, false information dissemination, and identity theft. The Deepfake-Audio-Detection-MaRS project addresses this challenge by building a complete machine learning pipeline specifically to distinguish between AI-generated and human real speech.

The core idea of the project is to convert audio signals into MFCC features (simulating human auditory perception and capturing key information such as timbre and pitch) and combine them with machine learning classifiers to achieve detection. The project codebase adopts a layered structure: .vscode/ (configuration), app/ (Streamlit application), model/ (model files), notebooks/ (experimental code), src/ (core source code), etc., which is convenient for maintenance and expansion.

1. Data Exploration: Analyze distribution characteristics such as audio duration, sampling rate, ratio of real to synthesized speech, and quality; 2. Preprocessing: Unify sampling rate, remove silence, normalize volume, and perform segment processing; 3. MFCC Extraction: Pre-emphasis → Framing and windowing → FFT → Mel filter bank → Logarithmic operation → DCT, extract 13/40-dimensional features and their differences; 4. Model Training: Adopt traditional ML (SVM, Random Forest, etc.) or deep learning (CNN, LSTM, etc.), split datasets, conduct cross-validation, and tune hyperparameters; 5. Streamlit Deployment: Provide a web interface that supports audio upload, real-time viewing of detection results and confidence levels.

• Evolution of Generative Technology: Countermeasures include continuously updating training data, adversarial training to enhance robustness, and combining multiple features; - Audio Quality Differences: Data augmentation (adding noise/reverb, etc.), multi-resolution features, domain adaptation; - Real-time Requirements: Optimize feature extraction, use lightweight models, edge deployment.

• Financial Industry: Bank identity verification, prevention of voice fraud, transaction authorization verification; - Media Platforms: Mark suspected synthesized content, assist content review, protect creators' rights; - Forensic Investigation: Court evidence verification, audio recording identification, combating forgery crimes; - Enterprise Security: Internal communication auditing, prevention of commercial fraud, protection of executives' voice commands.

The project value includes: 1. Educational Significance: An end-to-end audio classification project example; 2. Engineering Reference: Demonstrates code organization and modular design for ML projects; 3. Technical Foundation: Can serve as a framework for complex detection systems; 4. Security Awareness: Reminds people to pay attention to the identification of AI-generated content.

Summary: The project has a clear structure and complete functions, adopts the MFCC + machine learning solution, is stable and reliable, and easy to understand and deploy. It is a good reference for getting started with audio classification and Deepfake detection.

Future Directions: At the feature level: introduce more audio features, end-to-end deep learning, and integrate visual information; At the model level: try Transformer/Conformer, ensemble learning, and uncertainty estimation; At the system level: support real-time stream detection, develop API services, and build large-scale datasets.