Cybersecurity · advanced
Deepfake Audio Detection System for VoIP Security
A real-time audio analysis engine that detects synthesized or cloned voices in VoIP streams using MFCC features.
Project overview
Voice phishing (vishing) using AI clones is a top-tier threat to corporate security today. Building this project proves you can handle advanced, multi-modal threats and possess the technical depth required for specialized Cyber Defense and Forensics roles.
Real-world use case
A corporate security team implements this verification layer to authenticate high-value wire transfer approvals made over voice calls, protecting the organization from AI-cloned executive impersonation.
Tools and technologies
- Python
- Librosa
- TensorFlow/PyTorch
- FastAPI
Skills you can demonstrate
Audio Signal Processing, Deep Learning, VoIP Security
Build roadmap
- Collect your audio dataset
Download ASVspoof or LibriSpeech for real voices; generate fake samples with ElevenLabs or PlayHT free tier. - Extract MFCC features
Use librosa to extract Mel-frequency cepstral coefficients, pitch variance, and spectral features from each sample. - Train the detection model
Start with a Random Forest for a fast baseline, then try a small CNN on mel-spectrograms. Track accuracy on held-out data. - Build the FastAPI service
Create an /analyze endpoint that accepts audio uploads and returns a Human vs. AI confidence score. - Make it real-time capable
Refactor the audio processing pipeline with async to keep latency under 200ms — critical for VoIP use cases. - Deploy and demo
Host on Railway or Render. Create a simple upload page so recruiters can test it with their own audio clips.
Ways to extend the project
Integrate a real-time transcription service to automatically flag suspicious keywords and social engineering patterns often used in vishing attacks.
How to showcase your work
- GitHub: Detailed docs explaining the spectrogram analysis and feature engineering process.
- LinkedIn: Deploy a web app where recruiters can upload audio and receive a "Human vs. AI" confidence score.
- Resume: Highlight multi-modal threat detection and real-time audio analysis.