# MFAAN: Unveiling Audio Deepfakes with a Multi-Feature Authenticity Network

arXiv: https://arxiv.org/abs/2311.03509 · HTML: https://mlsanity.com/paper/2311.03509/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 6 Nov 2023.

## Builds on (most cited first, 5 of 5)

- [WaveFake: A Data Set to Facilitate Audio Deepfake Detection](https://mlsanity.com/paper/2111.02813/) (2021)
- [DeepSonar: Towards Effective and Robust Detection of AI-Synthesized Fake Voices](https://mlsanity.com/paper/2005.13770/) (2020)
- [Vision Transformer based COVID-19 Detection using Chest X-rays](https://mlsanity.com/paper/2110.04458/) (2021)
- [SwiftSRGAN -- Rethinking Super-Resolution for Efficient and Real-time Inference](https://mlsanity.com/paper/2111.14320/) (2021)
- [Benchmarking Conventional Vision Models on Neuromorphic Fall Detection and Action Recognition Dataset](https://mlsanity.com/paper/2201.12285/) (2022)

## Built on by (most cited first, 2 of 2)

- [Fake Artificial Intelligence Generated Contents (FAIGC): A Survey of Theories, Detection Methods, and Opportunities](https://mlsanity.com/paper/2405.00711/) (2024)
- [Half-Truth Audio Detection and Localisation: A Lightweight Cross-Attentive Architecture and a Cross-Corpus Diagnostic Study](https://mlsanity.com/paper/2605.29531/) (2026)

## Abstract (arXiv)

In the contemporary digital age, the proliferation of deepfakes presents a formidable challenge to the sanctity of information dissemination. Audio deepfakes, in particular, can be deceptively realistic, posing significant risks in misinformation campaigns. To address this threat, we introduce the Multi-Feature Audio Authenticity Network (MFAAN), an advanced architecture tailored for the detection of fabricated audio content. MFAAN incorporates multiple parallel paths designed to harness the strengths of different audio representations, including Mel-frequency cepstral coefficients (MFCC), linear-frequency cepstral coefficients (LFCC), and Chroma Short Time Fourier Transform (Chroma-STFT). By synergistically fusing these features, MFAAN achieves a nuanced understanding of audio content, facilitating robust differentiation between genuine and manipulated recordings. Preliminary evaluations of MFAAN on two benchmark datasets, 'In-the-Wild' Audio Deepfake Data and The Fake-or-Real Dataset, demonstrate its superior performance, achieving accuracies of 98.93% and 94.47% respectively. Such results not only underscore the efficacy of MFAAN but also highlight its potential as a pivotal tool in the ongoing battle against deepfake audio content.