# How Deep Are the Fakes? Focusing on Audio Deepfake: A Survey

arXiv: https://arxiv.org/abs/2111.14203 · HTML: https://mlsanity.com/paper/2111.14203/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 77 references that are in the map.
- First posted on arXiv 28 Nov 2021.

## Builds on (most cited first, 30 of 35)

- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions](https://mlsanity.com/paper/1712.05884/) (2017)
- [MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis](https://mlsanity.com/paper/1910.06711/) (2019)
- [Everybody Dance Now](https://mlsanity.com/paper/1808.07371/) (2018)
- [WaveGlow: A Flow-based Generative Network for Speech Synthesis](https://mlsanity.com/paper/1811.00002/) (2018)
- [Few-Shot Adversarial Learning of Realistic Neural Talking Head Models](https://mlsanity.com/paper/1905.08233/) (2019)
- [Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis](https://mlsanity.com/paper/1806.04558/) (2018)
- [The Creation and Detection of Deepfakes: A Survey](https://mlsanity.com/paper/2004.11138/) (2020)
- [DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection](https://mlsanity.com/paper/2001.00179/) (2020)
- [Emotions Don't Lie: An Audio-Visual Deepfake Detection Method Using Affective Cues](https://mlsanity.com/paper/2003.06711/) (2020)
- [Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis](https://mlsanity.com/paper/1601.04589/) (2016)
- [Deep Learning for Deepfakes Creation and Detection: A Survey](https://mlsanity.com/paper/1909.11573/) (2019)
- [Deep Voice: Real-time Neural Text-to-Speech](https://mlsanity.com/paper/1702.07825/) (2017)
- [StarGAN-VC: Non-parallel many-to-many voice conversion with star generative adversarial networks](https://mlsanity.com/paper/1806.02169/) (2018)
- [Deep Voice 2: Multi-Speaker Neural Text-to-Speech](https://mlsanity.com/paper/1705.08947/) (2017)
- [One-class Learning Towards Synthetic Voice Spoofing Detection](https://mlsanity.com/paper/2010.13995/) (2020)
- [Fully Character-Level Neural Machine Translation without Explicit Segmentation](https://mlsanity.com/paper/1610.03017/) (2016)
- [MelNet: A Generative Model for Audio in the Frequency Domain](https://mlsanity.com/paper/1906.01083/) (2019)
- [Deep Residual Neural Networks for Audio Spoofing Detection](https://mlsanity.com/paper/1907.00501/) (2019)
- [TextGAIL: Generative Adversarial Imitation Learning for Text Generation](https://mlsanity.com/paper/2004.13796/) (2020)
- [Non-Parallel Sequence-to-Sequence Voice Conversion with Disentangled Linguistic and Speaker Representations](https://mlsanity.com/paper/1906.10508/) (2019)
- [Voice Impersonation using Generative Adversarial Networks](https://mlsanity.com/paper/1802.06840/) (2018)
- [Statistical Parametric Speech Synthesis Incorporating Generative Adversarial Networks](https://mlsanity.com/paper/1709.08041/) (2017)
- [High-quality nonparallel voice conversion based on cycle-consistent adversarial network](https://mlsanity.com/paper/1804.00425/) (2018)
- [Attentive Filtering Networks for Audio Replay Attack Detection](https://mlsanity.com/paper/1810.13048/) (2018)

## Built on by (most cited first, 6 of 6)

- [GAN-generated Faces Detection: A Survey and New Perspectives](https://mlsanity.com/paper/2202.07145/) (2022)
- [XAI-Based Detection of Adversarial Attacks on Deepfake Detectors](https://mlsanity.com/paper/2403.02955/) (2024)
- [VoiceWukong: Benchmarking Deepfake Voice Detection](https://mlsanity.com/paper/2409.06348/) (2024)
- [Reliable Audio Deepfake Detection in Variable Conditions via Quantum-Kernel SVMs](https://mlsanity.com/paper/2512.18797/) (2025)
- [Quantum Kernels for Audio Deepfake Detection Using Spectrogram Patch Features](https://mlsanity.com/paper/2605.06035/) (2026)
- [Deep Fake Detection, Deterrence and Response: Challenges and Opportunities](https://mlsanity.com/paper/2211.14667/) (2022)

## Abstract (arXiv)

Deepfake is content or material that is synthetically generated or manipulated using artificial intelligence (AI) methods, to be passed off as real and can include audio, video, image, and text synthesis. This survey has been conducted with a different perspective compared to existing survey papers, that mostly focus on just video and image deepfakes. This survey not only evaluates generation and detection methods in the different deepfake categories, but mainly focuses on audio deepfakes that are overlooked in most of the existing surveys. This paper critically analyzes and provides a unique source of audio deepfake research, mostly ranging from 2016 to 2020. To the best of our knowledge, this is the first survey focusing on audio deepfakes in English. This survey provides readers with a summary of 1) different deepfake categories 2) how they could be created and detected 3) the most recent trends in this domain and shortcomings in detection methods 4) audio deepfakes, how they are created and detected in more detail which is the main focus of this paper. We found that Generative Adversarial Networks(GAN), Convolutional Neural Networks (CNN), and Deep Neural Networks (DNN) are common ways of creating and detecting deepfakes. In our evaluation of over 140 methods we found that the majority of the focus is on video deepfakes and in particular in the generation of video deepfakes. We found that for text deepfakes there are more generation methods but very few robust methods for detection, including fake news detection, which has become a controversial area of research because of the potential of heavy overlaps with human generation of fake content. This paper is an abbreviated version of the full survey and reveals a clear need to research audio deepfakes and particularly detection of audio deepfakes.