# ADD 2022: the First Audio Deep Synthesis Detection Challenge

arXiv: https://arxiv.org/abs/2202.08433 · HTML: https://mlsanity.com/paper/2202.08433/

## Key facts

- Cited by 96 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 20 references that are in the map.
- First posted on arXiv 17 Feb 2022.

## Builds on (most cited first, 14 of 14)

- [Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions](https://mlsanity.com/paper/1712.05884/) (2017)
- [Tacotron: Towards End-to-End Speech Synthesis](https://mlsanity.com/paper/1703.10135/) (2017)
- [AISHELL-1: An Open-Source Mandarin Speech Corpus and A Speech Recognition Baseline](https://mlsanity.com/paper/1709.05522/) (2017)
- [Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis](https://mlsanity.com/paper/1803.09017/) (2018)
- [ASVspoof 2019: Future Horizons in Spoofed and Fake Audio Detection](https://mlsanity.com/paper/1904.05441/) (2019)
- [ASVspoof 2021: accelerating progress in spoofed and deepfake speech detection](https://mlsanity.com/paper/2109.00537/) (2021)
- [AISHELL-3: A Multi-speaker Mandarin TTS Corpus and the Baselines](https://mlsanity.com/paper/2010.11567/) (2020)
- [AISHELL-4: An Open Source Dataset for Speech Enhancement, Separation, Recognition and Speaker Diarization in Conference Scenario](https://mlsanity.com/paper/2104.03603/) (2021)
- [Half-Truth: A Partially Fake Audio Detection Dataset](https://mlsanity.com/paper/2104.03617/) (2021)
- [Continual Learning for Fake Audio Detection](https://mlsanity.com/paper/2104.07286/) (2021)
- [DeepSonar: Towards Effective and Robust Detection of AI-Synthesized Fake Voices](https://mlsanity.com/paper/2005.13770/) (2020)
- [Light Convolutional Neural Network with Feature Genuinization for Detection of Synthetic Speech Attacks](https://mlsanity.com/paper/2009.09637/) (2020)
- [Improved RawNet with Feature Map Scaling for Text-independent Speaker Verification using Raw Waveforms](https://mlsanity.com/paper/2004.00526/) (2020)
- [DFGC 2021: A DeepFake Game Competition](https://mlsanity.com/paper/2106.01217/) (2021)

## Built on by (most cited first, 30 of 96)

- [AV-Deepfake1M: A Large-Scale LLM-Driven Audio-Visual Deepfake Dataset](https://mlsanity.com/paper/2311.15308/) (2023)
- [The Codecfake Dataset and Countermeasures for the Universally Detection of Deepfake Audio](https://mlsanity.com/paper/2405.04880/) (2024)
- [SpoofCeleb: Speech Deepfake Detection and SASV In The Wild](https://mlsanity.com/paper/2409.17285/) (2024)
- [Speech DF Arena: A Leaderboard for Speech DeepFake Detection Models](https://mlsanity.com/paper/2509.02859/) (2025)
- [ALLM4ADD: Unlocking the Capabilities of Audio Large Language Models for Audio Deepfake Detection](https://mlsanity.com/paper/2505.11079/) (2025)
- [What to Remember: Self-Adaptive Continual Learning for Audio Deepfake Detection](https://mlsanity.com/paper/2312.09651/) (2023)
- [Leveraging Mixture of Experts for Improved Speech Deepfake Detection](https://mlsanity.com/paper/2409.16077/) (2024)
- [An Initial Investigation for Detecting Vocoder Fingerprints of Fake Audio](https://mlsanity.com/paper/2208.09646/) (2022)
- [FSD: An Initial Chinese Dataset for Fake Song Detection](https://mlsanity.com/paper/2309.02232/) (2023)
- [Do You Remember? Overcoming Catastrophic Forgetting for Fake Audio Detection](https://mlsanity.com/paper/2308.03300/) (2023)
- [Cross-Domain Audio Deepfake Detection: Dataset and Analysis](https://mlsanity.com/paper/2404.04904/) (2024)
- [Where are we in audio deepfake detection? A systematic analysis over generative and detection models](https://mlsanity.com/paper/2410.04324/) (2024)
- [Fully Automated End-to-End Fake Audio Detection](https://mlsanity.com/paper/2208.09618/) (2022)
- [Single and Multi-Speaker Cloned Voice Detection: From Perceptual to Learned Features](https://mlsanity.com/paper/2307.07683/) (2023)
- [An Efficient Temporary Deepfake Location Approach Based Embeddings for Partially Spoofed Audio Detection](https://mlsanity.com/paper/2309.03036/) (2023)
- [How Do Neural Spoofing Countermeasures Detect Partially Spoofed Audio?](https://mlsanity.com/paper/2406.02483/) (2024)
- [Deepfake Media Generation and Detection in the Generative AI Era: A Survey and Outlook](https://mlsanity.com/paper/2411.19537/) (2024)
- [Audio Deepfake Detection Based on a Combination of F0 Information and Real Plus Imaginary Spectrogram Features](https://mlsanity.com/paper/2208.01214/) (2022)
- [The defender's perspective on automatic speaker verification: An overview](https://mlsanity.com/paper/2305.12804/) (2023)
- [CLAD: Robust Audio Deepfake Detection Against Manipulation Attacks with Contrastive Learning](https://mlsanity.com/paper/2404.15854/) (2024)
- [Enhancing Partially Spoofed Audio Localization with Boundary-aware Attention Mechanism](https://mlsanity.com/paper/2407.21611/) (2024)
- [Forensic deepfake audio detection using segmental speech features](https://mlsanity.com/paper/2505.13847/) (2025)
- [An Overview of Recent Work in Media Forensics: Methods and Threats](https://mlsanity.com/paper/2204.12067/) (2022)
- [Collaborative Watermarking for Adversarial Speech Synthesis](https://mlsanity.com/paper/2309.15224/) (2023)
- [Multilingual Source Tracing of Speech Deepfakes: A First Benchmark](https://mlsanity.com/paper/2508.04143/) (2025)
- [Deepfake: Definitions, Performance Metrics and Standards, Datasets and Benchmarks, and a Meta-Review](https://mlsanity.com/paper/2208.10913/) (2022)
- [Codecfake: An Initial Dataset for Detecting LLM-based Deepfake Audio](https://mlsanity.com/paper/2406.08112/) (2024)
- [SCDF: A Speaker Characteristics DeepFake Speech Dataset for Bias Analysis](https://mlsanity.com/paper/2508.07944/) (2025)
- [SceneFake: An Initial Dataset and Benchmarks for Scene Fake Audio Detection](https://mlsanity.com/paper/2211.06073/) (2022)
- [Range-Based Equal Error Rate for Spoof Localization](https://mlsanity.com/paper/2305.17739/) (2023)

## Abstract (arXiv)

Audio deepfake detection is an emerging topic, which was included in the ASVspoof 2021. However, the recent shared tasks have not covered many real-life and challenging scenarios. The first Audio Deep synthesis Detection challenge (ADD) was motivated to fill in the gap. The ADD 2022 includes three tracks: low-quality fake audio detection (LF), partially fake audio detection (PF) and audio fake game (FG). The LF track focuses on dealing with bona fide and fully fake utterances with various real-world noises etc. The PF track aims to distinguish the partially fake audio from the real. The FG track is a rivalry game, which includes two tasks: an audio generation task and an audio fake detection task. In this paper, we describe the datasets, evaluation metrics, and protocols. We also report major findings that reflect the recent advances in audio deepfake detection tasks.