detecting-deepfake-audio-in-vishing-attacks

# Detecting Deepfake Audio in Vishing Attacks ## When to Use - A suspected vishing call used an AI-cloned executive voice to authorize a wire transfer - Security operations received a voicemail that sounds like the CEO but the tone seems off - Incident response needs to determine whether a recorded phone call contains synthetic speech - Fraud investigation requires forensic proof that audio was AI-generated - Red team exercises use voice cloning and blue team needs detection capability **Do not use** for text-based phishing (email/SMS); use email header analysis or URL detonation tools instead. ## Prerequisites - Python 3.9+ with librosa, numpy, scikit-learn, and scipy installed - Audio samples in WAV, MP3, or FLAC format (mono or stereo, any sample rate) - Reference corpus of known genuine voice samples for the targeted individual (optional but improves accuracy) - FFmpeg installed for audio format conversion (librosa dependency) - Minimum 3 seconds of audio for reliable feature extraction ## Workflow ### Step 1: Audio Preprocessing Normalize and prepare audio samples for feature extraction: ```python import librosa import numpy as np # Load audio, resample to 16kHz mono y, sr = librosa.load("suspect_call.wav", sr=16000, mono=True) # Trim silence from beginning and end y_trimmed, _ = librosa.effects.trim(y, top_db=25) # Normalize amplitude to [-1, 1] y_norm = y_trimmed / np.max(np.abs(y_trimmed)) ``` Audio preprocessing ensures consistent feature extraction acro...