detecting-deepfake-audio-in-vishing-attacks

Load the audio with librosa at 16kHz mono, extract MFCCs and spectral features, then classify with a Random Forest and Gradient Boosting ensemble. Deepfake audio typically shows reduced spectral contrast in the 4-8 kHz range and abnormally low pitch jitter.

Key indicators include reduced spectral contrast above 4 kHz, unnaturally stable pitch with low jitter and shimmer, energy cutoffs at the vocoder ceiling around 7-8 kHz, and banding patterns in mel spectrograms at fixed intervals.

It works but with reduced accuracy because phone codecs like G.711 and AMR compress high frequencies where vocoder artifacts appear. Analyze at the highest available fidelity and account for codec effects when interpreting confidence scores.

The workflow requires librosa for feature extraction, scikit-learn for classification, numpy and scipy for numerical processing, and FFmpeg for audio format conversion. Optional tools include Resemblyzer for speaker embeddings and Praat for phonetic analysis.

Clips under 3 seconds produce unreliable feature statistics, and fine-tuned models like VALL-E trained on 30+ minutes of target audio can evade basic MFCC analysis. Confirm verdicts with out-of-band verification such as calling back on a known number.