# Manipulated Regions Localization For Partially Deepfake Audio: A Survey

arXiv: https://arxiv.org/abs/2506.14396 · HTML: https://mlsanity.com/paper/2506.14396/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2025.

## Built on by (most cited first, 5 of 5)

- [TRACE: Training-Free Partial Audio Deepfake Detection via Embedding Trajectory Analysis of Speech Foundation Models](https://mlsanity.com/paper/2604.01083/) (2026)
- [Localizing Audio-Visual Deepfakes via Hierarchical Boundary Modeling](https://mlsanity.com/paper/2508.02000/) (2025)
- [Alethia: A Foundational Encoder for Voice Deepfakes](https://mlsanity.com/paper/2605.00251/) (2026)
- [A Training-Free Proactive Defense Against Partial Speech Manipulation via Self-Embedding Steganography](https://mlsanity.com/paper/2608.25285/) (2026)
- [Combining Self-Embedding Audio Watermarking with Ultra-Low-Bitrate Neural Codecs](https://mlsanity.com/paper/2608.25289/) (2026)

## Abstract (arXiv)

With the development of audio deepfake techniques, attacks with partially deepfake audio are beginning to rise. Compared to fully deepfake, it is much harder to be identified by the detector due to the partially cryptic manipulation, resulting in higher security risks. Although some studies have been launched, there is no comprehensive review to systematically introduce the current situations and development trends for addressing this issue. Thus, in this survey, we are the first to outline a systematic introduction for partially deepfake audio manipulated region localization tasks, including the fundamentals, branches of existing methods, current limitations and potential trends, providing a revealing insight into this scope.