VOICE GUARD AI is an AI-powered voice security and fraud detection project designed to address the growing problem of voice-based scams and AI-generated/deepfake voices. I was inspired by how easily modern AI can imitate human voices and how difficult it can be for people to determine whether a voice recording is genuine or manipulated. I built the MVP as a voice forensics system where a user can upload an audio recording and receive multiple layers of analysis. The system analyses the audio for potential synthetic/deepfake characteristics, converts the speech into text, and examines the transcript for potential fraud or scam-related signals. It also provides audio information, waveform and spectrogram visualizations, risk indicators, and a SHA-256 file hash that can be used as an evidence identifier. The project is built using Python and Streamlit, with separate modules for audio analysis, deepfake detection, transcription, and fraud-risk analysis. I designed the system as a modular pipeline so that each part can be improved or replaced independently as the project develops. Through this project, I learned how different AI components can be combined into a single practical application. I also learned about audio processing, machine-learning-based detection, speech transcription, risk analysis, data handling, visualization, and designing an AI system that communicates probabilistic results rather than treating them as absolute truth. One of the biggest challenges was connecting multiple analysis stages reliably while keeping the application understandable and useful to the user. Audio formats, model outputs, processing time, and inconsistent results required careful handling. Designing the interface was another challenge because I wanted the project to feel like a real cybersecurity and digital-forensics tool rather than simply an audio upload page. VOICE GUARD AI is currently an MVP focused on uploaded audio analysis. My long-term goal is to develop it into a mobile voice-security platform capable of analysing voice interactions in real time and providing users with early warnings about possible AI-generated voices or fraud attempts.
Built With
- ai
- analysis
- artificial
- audio
- computer
- cybercrime
- cybersecurity
- data
- detection
- digital
- forensics
- fraud
- intelligence
- language
- learning
- machine
- processing
- python
- recognition
- security
- speech
- speech-to-text
- streamlit
- voice
Log in or sign up for Devpost to join the conversation.