# Training-Free Deepfake Voice Recognition by Leveraging Large-Scale Pre-Trained Models

arXiv: https://arxiv.org/abs/2405.02179 · HTML: https://mlsanity.com/paper/2405.02179/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 36 references that are in the map.
- First posted on arXiv 3 May 2024.

## Builds on (most cited first, 24 of 24)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation](https://mlsanity.com/paper/2211.06687/) (2022)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [Towards Universal Fake Image Detectors that Generalize Across Generative Models](https://mlsanity.com/paper/2302.10174/) (2023)
- [BEATs: Audio Pre-Training with Acoustic Tokenizers](https://mlsanity.com/paper/2212.09058/) (2022)
- [AudioCLIP: Extending CLIP to Image, Text and Audio](https://mlsanity.com/paper/2106.13043/) (2021)
- [End-to-end anti-spoofing with RawNet2](https://mlsanity.com/paper/2011.01108/) (2020)
- [Raising the Bar of AI-generated Image Detection with CLIP](https://mlsanity.com/paper/2312.00195/) (2023)
- [ASVspoof 2021: accelerating progress in spoofed and deepfake speech detection](https://mlsanity.com/paper/2109.00537/) (2021)
- [Does Audio Deepfake Detection Generalize?](https://mlsanity.com/paper/2203.16263/) (2022)
- [ID-Reveal: Identity-aware DeepFake Video Detection](https://mlsanity.com/paper/2012.02512/) (2020)
- [End-to-End Spectro-Temporal Graph Attention Networks for Speaker Verification Anti-Spoofing and Speech Deepfake Detection](https://mlsanity.com/paper/2107.12710/) (2021)
- [TitaNet: Neural Model for speaker representation with 1D Depth-wise separable convolutions and global context](https://mlsanity.com/paper/2110.04410/) (2021)
- [One-class Learning Towards Synthetic Voice Spoofing Detection](https://mlsanity.com/paper/2010.13995/) (2020)
- [Audio-Visual Person-of-Interest DeepFake Detection](https://mlsanity.com/paper/2204.03083/) (2022)
- [ESResNe(X)t-fbsp: Learning Robust Time-Frequency Transformation of Audio](https://mlsanity.com/paper/2104.11587/) (2021)
- [Towards generalisable and calibrated synthetic speech detection with self-supervised representations](https://mlsanity.com/paper/2309.05384/) (2023)
- [SAMO: Speaker Attractor Multi-Center One-Class Learning for Voice Anti-Spoofing](https://mlsanity.com/paper/2211.02718/) (2022)
- [Deepfake audio detection by speaker verification](https://mlsanity.com/paper/2209.14098/) (2022)
- [Single and Multi-Speaker Cloned Voice Detection: From Perceptual to Learned Features](https://mlsanity.com/paper/2307.07683/) (2023)
- [Baseline Systems for the First Spoofing-Aware Speaker Verification Challenge: Score and Embedding Fusion](https://mlsanity.com/paper/2204.09976/) (2022)

## Built on by (most cited first, 8 of 8)

- [Circumventing shortcuts in audio-visual deepfake detection datasets with unsupervised learning](https://mlsanity.com/paper/2412.00175/) (2024)
- [DeCLIP: Decoding CLIP representations for deepfake localization](https://mlsanity.com/paper/2409.08849/) (2024)
- [The DeepSpeak Dataset](https://mlsanity.com/paper/2408.05366/) (2024)
- [Improving Out-of-Domain Audio Deepfake Detection via Layer Selection and Fusion of SSL-Based Countermeasures](https://mlsanity.com/paper/2509.12003/) (2025)
- [The Tug-of-War Between Deepfake Generation and Detection](https://mlsanity.com/paper/2407.06174/) (2024)
- [Investigating self-supervised representations for audio-visual deepfake detection](https://mlsanity.com/paper/2511.17181/) (2025)
- [SAVe: Self-Supervised Audio-visual Deepfake Detection Exploiting Visual Artifacts and Audio-visual Misalignment](https://mlsanity.com/paper/2603.25140/) (2026)
- [Deepfake Audio Detection Using Self-supervised Fusion Representations](https://mlsanity.com/paper/2605.03420/) (2026)

## Abstract (arXiv)

Generalization is a main issue for current audio deepfake detectors, which struggle to provide reliable results on out-of-distribution data. Given the speed at which more and more accurate synthesis methods are developed, it is very important to design techniques that work well also on data they were not trained for. In this paper we study the potential of large-scale pre-trained models for audio deepfake detection, with special focus on generalization ability. To this end, the detection problem is reformulated in a speaker verification framework and fake audios are exposed by the mismatch between the voice sample under test and the voice of the claimed identity. With this paradigm, no fake speech sample is necessary in training, cutting off any link with the generation method at the root, and ensuring full generalization ability. Features are extracted by general-purpose large pre-trained models, with no need for training or fine-tuning on specific fake detection or speaker verification datasets. At detection time only a limited set of voice fragments of the identity under test is required. Experiments on several datasets widespread in the community show that detectors based on pre-trained models achieve excellent performance and show strong generalization ability, rivaling supervised methods on in-distribution data and largely overcoming them on out-of-distribution data.