# CFAD: A Chinese Dataset for Fake Audio Detection

arXiv: https://arxiv.org/abs/2207.12308 · HTML: https://mlsanity.com/paper/2207.12308/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2022.

## Built on by (most cited first, 14 of 14)

- [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)
- [Do You Remember? Overcoming Catastrophic Forgetting for Fake Audio Detection](https://mlsanity.com/paper/2308.03300/) (2023)
- [Where are we in audio deepfake detection? A systematic analysis over generative and detection models](https://mlsanity.com/paper/2410.04324/) (2024)
- [Harder or Different? Understanding Generalization of Audio Deepfake Detection](https://mlsanity.com/paper/2406.03512/) (2024)
- [Deepfake: Definitions, Performance Metrics and Standards, Datasets and Benchmarks, and a Meta-Review](https://mlsanity.com/paper/2208.10913/) (2022)
- [Fake Speech Wild: Detecting Deepfake Speech on Social Media Platform](https://mlsanity.com/paper/2508.10559/) (2025)
- [Alethia: A Foundational Encoder for Voice Deepfakes](https://mlsanity.com/paper/2605.00251/) (2026)
- [Ethical and Technical Limits of Deepfake Speech Datasets](https://mlsanity.com/paper/2606.10911/) (2026)
- [Rehearsal with Auxiliary-Informed Sampling for Audio Deepfake Detection](https://mlsanity.com/paper/2505.24486/) (2025)
- [HISPASpoof: A New Dataset For Spanish Speech Forensics](https://mlsanity.com/paper/2509.09155/) (2025)
- [Probabilistic Verification of Voice Anti-Spoofing Models](https://mlsanity.com/paper/2603.10713/) (2026)
- [Spectro-Temporal Modulation Representation Framework for Human-Imitated Speech Detection](https://mlsanity.com/paper/2604.23241/) (2026)
- [A Comparison of SSL-Based Feature Extractors and Back-End Classifiers for Spoofing Detection: A Multi-Corpus Training and Cross-Linguistic Analysis](https://mlsanity.com/paper/2606.08669/) (2026)

## Abstract (arXiv)

Fake audio detection is a growing concern and some relevant datasets have been designed for research. However, there is no standard public Chinese dataset under complex conditions.In this paper, we aim to fill in the gap and design a Chinese fake audio detection dataset (CFAD) for studying more generalized detection methods. Twelve mainstream speech-generation techniques are used to generate fake audio. To simulate the real-life scenarios, three noise datasets are selected for noise adding at five different signal-to-noise ratios, and six codecs are considered for audio transcoding (format conversion). CFAD dataset can be used not only for fake audio detection but also for detecting the algorithms of fake utterances for audio forensics. Baseline results are presented with analysis. The results that show fake audio detection methods with generalization remain challenging. The CFAD dataset is publicly available at: https://zenodo.org/record/8122764.