# Detect All-Type Deepfake Audio: Wavelet Prompt Tuning for Enhanced Auditory Perception

arXiv: https://arxiv.org/abs/2504.06753 · HTML: https://mlsanity.com/paper/2504.06753/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 51 references that are in the map.
- First posted on arXiv 9 Apr 2025.

## Builds on (most cited first, 30 of 37)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Visual Prompt Tuning](https://mlsanity.com/paper/2203.12119/) (2022)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [Vision Transformers Need Registers](https://mlsanity.com/paper/2309.16588/) (2023)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [MusicLM: Generating Music From Text](https://mlsanity.com/paper/2301.11325/) (2023)
- [A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark](https://mlsanity.com/paper/1910.04867/) (2019)
- [AudioGen: Textually Guided Audio Generation](https://mlsanity.com/paper/2209.15352/) (2022)
- [MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training](https://mlsanity.com/paper/2306.00107/) (2023)
- [AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks](https://mlsanity.com/paper/2110.01200/) (2021)
- [ASVspoof 2019: Future Horizons in Spoofed and Fake Audio Detection](https://mlsanity.com/paper/1904.05441/) (2019)
- [Speaker Recognition from Raw Waveform with SincNet](https://mlsanity.com/paper/1808.00158/) (2018)
- [ASVspoof 2021: Towards Spoofed and Deepfake Speech Detection in the Wild](https://mlsanity.com/paper/2210.02437/) (2022)
- [Automatic speaker verification spoofing and deepfake detection using wav2vec 2.0 and data augmentation](https://mlsanity.com/paper/2202.12233/) (2022)
- [ASVspoof 5: Crowdsourced Speech Data, Deepfakes, and Adversarial Attacks at Scale](https://mlsanity.com/paper/2408.08739/) (2024)
- [Improving Visual Prompt Tuning for Self-supervised Vision Transformers](https://mlsanity.com/paper/2306.05067/) (2023)
- [Multitask Vision-Language Prompt Tuning](https://mlsanity.com/paper/2211.11720/) (2022)
- [The Codecfake Dataset and Countermeasures for the Universally Detection of Deepfake Audio](https://mlsanity.com/paper/2405.04880/) (2024)
- [Towards generalisable and calibrated synthetic speech detection with self-supervised representations](https://mlsanity.com/paper/2309.05384/) (2023)
- [Mixture of Experts Fusion for Fake Audio Detection Using Frozen wav2vec 2.0](https://mlsanity.com/paper/2409.11909/) (2024)
- [Attentive Merging of Hidden Embeddings from Pre-trained Speech Model for Anti-spoofing Detection](https://mlsanity.com/paper/2406.10283/) (2024)
- [CtrSVDD: A Benchmark Dataset and Baseline Analysis for Controlled Singing Voice Deepfake Detection](https://mlsanity.com/paper/2406.02438/) (2024)
- [Heterogeneity over Homogeneity: Investigating Multilingual Speech Pre-Trained Models for Detecting Audio Deepfake](https://mlsanity.com/paper/2404.00809/) (2024)
- [The Impact of Silence on Speech Anti-Spoofing](https://mlsanity.com/paper/2309.11827/) (2023)
- [FakeMusicCaps: a Dataset for Detection and Attribution of Synthetic Music Generated via Text-to-Music Models](https://mlsanity.com/paper/2409.10684/) (2024)
- [I Can Hear You: Selective Robust Training for Deepfake Audio Detection](https://mlsanity.com/paper/2411.00121/) (2024)
- [Comprehensive Layer-wise Analysis of SSL Models for Audio Deepfake Detection](https://mlsanity.com/paper/2502.03559/) (2025)
- [Defense Against Adversarial Attacks on Audio DeepFake Detection](https://mlsanity.com/paper/2212.14597/) (2022)
- [FSD: An Initial Chinese Dataset for Fake Song Detection](https://mlsanity.com/paper/2309.02232/) (2023)
- [SpeechPrompt: Prompting Speech Language Models for Speech Processing Tasks](https://mlsanity.com/paper/2408.13040/) (2024)

## Built on by (most cited first, 7 of 7)

- [Interpretable All-Type Audio Deepfake Detection with Audio LLMs via Frequency-Time Reinforcement Learning](https://mlsanity.com/paper/2601.02983/) (2026)
- [WaveSP-Net: Learnable Wavelet-Domain Sparse Prompt Tuning for Speech Deepfake Detection](https://mlsanity.com/paper/2510.05305/) (2025)
- [WaveScat: Wavelet Scattering Front-Ends with Self-Supervised Features for Speech Deepfake Detection](https://mlsanity.com/paper/2602.02980/) (2026)
- [AT-ADD: All-Type Audio Deepfake Detection Challenge Evaluation Plan](https://mlsanity.com/paper/2604.08184/) (2026)
- [REIMU: Efficient Heterogeneous Hierarchical Reasoning for SSL-Based Speech Deepfake Detection](https://mlsanity.com/paper/2608.00857/) (2026)
- [ToolDF: Tool-Integrated Reasoning for Mixed-Authenticity Audio Deepfake Detection](https://mlsanity.com/paper/2609.03620/) (2026)
- [GLAD: Global-Local Adaptive Detector for Robust Speech Deepfake Detection](https://mlsanity.com/paper/2609.35411/) (2026)

## Abstract (arXiv)

The rapid advancement of audio generation technologies has escalated the risks of malicious deepfake audio across speech, sound, singing voice, and music, threatening multimedia security and trust. While existing countermeasures (CMs) perform well in single-type audio deepfake detection (ADD), their performance declines in cross-type scenarios. This paper is dedicated to studying the all-type ADD task. We are the first to comprehensively establish an all-type ADD benchmark to evaluate current CMs, incorporating cross-type deepfake detection across speech, sound, singing voice, and music. Then, we introduce the prompt tuning self-supervised learning (PT-SSL) training paradigm, which optimizes SSL front-end by learning specialized prompt tokens for ADD, requiring 458x fewer trainable parameters than fine-tuning (FT). Considering the auditory perception of different audio types, we propose the wavelet prompt tuning (WPT)-SSL method to capture type-invariant auditory deepfake information from the frequency domain without requiring additional training parameters, thereby enhancing performance over FT in the all-type ADD task. To achieve an universally CM, we utilize all types of deepfake audio for co-training. Experimental results demonstrate that WPT-XLSR-AASIST achieved the best performance, with an average EER of 3.58% across all evaluation sets.