# Continuous Learning of Transformer-based Audio Deepfake Detection

arXiv: https://arxiv.org/abs/2409.05924 · HTML: https://mlsanity.com/paper/2409.05924/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2024.

## Built on by (most cited first, 3 of 3)

- [Toward Robust Real-World Audio Deepfake Detection: Closing the Explainability Gap](https://mlsanity.com/paper/2410.07436/) (2024)
- [Multi-Speaker Conversational Audio Deepfake: Taxonomy, Dataset and Pilot Study](https://mlsanity.com/paper/2602.00295/) (2026)
- [Zero-Shot to Zero-Lies: Detecting Bengali Deepfake Audio through Transfer Learning](https://mlsanity.com/paper/2512.21702/) (2025)

## Abstract (arXiv)

This paper proposes a novel framework for audio deepfake detection with two main objectives: i) attaining the highest possible accuracy on available fake data, and ii) effectively performing continuous learning on new fake data in a few-shot learning manner. Specifically, we conduct a large audio deepfake collection using various deep audio generation methods. The data is further enhanced with additional augmentation methods to increase variations amidst compressions, far-field recordings, noise, and other distortions. We then adopt the Audio Spectrogram Transformer for the audio deepfake detection model. Accordingly, the proposed method achieves promising performance on various benchmark datasets. Furthermore, we present a continuous learning plugin module to update the trained model most effectively with the fewest possible labeled data points of the new fake type. The proposed method outperforms the conventional direct fine-tuning approach with much fewer labeled data points.