# Enhancing Partially Spoofed Audio Localization with Boundary-aware Attention Mechanism

arXiv: https://arxiv.org/abs/2407.21611 · HTML: https://mlsanity.com/paper/2407.21611/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 26 references that are in the map.
- First posted on arXiv 31 Jul 2024.

## Builds on (most cited first, 19 of 19)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [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)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [Self-Normalizing Neural Networks](https://mlsanity.com/paper/1706.02515/) (2017)
- [NaturalSpeech 2: Latent Diffusion Models are Natural and Zero-Shot Speech and Singing Synthesizers](https://mlsanity.com/paper/2304.09116/) (2023)
- [ASVspoof 2019: Future Horizons in Spoofed and Fake Audio Detection](https://mlsanity.com/paper/1904.05441/) (2019)
- [Attentive Statistics Pooling for Deep Speaker Embedding](https://mlsanity.com/paper/1803.10963/) (2018)
- [ADD 2022: the First Audio Deep Synthesis Detection Challenge](https://mlsanity.com/paper/2202.08433/) (2022)
- [FreeVC: Towards High-Quality Text-Free One-Shot Voice Conversion](https://mlsanity.com/paper/2210.15418/) (2022)
- [ADD 2023: the Second Audio Deepfake Detection Challenge](https://mlsanity.com/paper/2305.13774/) (2023)
- [The PartialSpoof Database and Countermeasures for the Detection of Short Fake Speech Segments Embedded in an Utterance](https://mlsanity.com/paper/2204.05177/) (2022)
- [Half-Truth: A Partially Fake Audio Detection Dataset](https://mlsanity.com/paper/2104.03617/) (2021)
- [An Initial Investigation for Detecting Partially Spoofed Audio](https://mlsanity.com/paper/2104.02518/) (2021)
- [Multi-Task Learning in Utterance-Level and Segmental-Level Spoof Detection](https://mlsanity.com/paper/2107.14132/) (2021)
- [Waveform Boundary Detection for Partially Spoofed Audio](https://mlsanity.com/paper/2211.00226/) (2022)
- [Partially Fake Audio Detection by Self-attention-based Fake Span Discovery](https://mlsanity.com/paper/2202.06684/) (2022)
- [An Efficient Temporary Deepfake Location Approach Based Embeddings for Partially Spoofed Audio Detection](https://mlsanity.com/paper/2309.03036/) (2023)
- [Deep Spectro-temporal Artifacts for Detecting Synthesized Speech](https://mlsanity.com/paper/2210.05254/) (2022)

## Built on by (most cited first, 9 of 9)

- [SZU-AFS Antispoofing System for the ASVspoof 5 Challenge](https://mlsanity.com/paper/2408.09933/) (2024)
- [TRACE: Training-Free Partial Audio Deepfake Detection via Embedding Trajectory Analysis of Speech Foundation Models](https://mlsanity.com/paper/2604.01083/) (2026)
- [Pindrop it! Audio and Visual Deepfake Countermeasures for Robust Detection and Fine Grained-Localization](https://mlsanity.com/paper/2508.08141/) (2025)
- [NE-PADD: Leveraging Named Entity Knowledge for Robust Partial Audio Deepfake Detection via Attention Aggregation](https://mlsanity.com/paper/2509.03829/) (2025)
- [Unifying Speech Editing Detection and Content Localization via Prior-Enhanced Audio LLMs](https://mlsanity.com/paper/2601.21463/) (2026)
- [Split and Conquer Partial Deepfake Speech](https://mlsanity.com/paper/2604.02913/) (2026)
- [Robust Spoofed Speech Detection via Temporal Pyramid Modeling](https://mlsanity.com/paper/2606.16837/) (2026)
- [Supervised Post-training of Speech Foundation Models for Robust Adaptation in Speech Deepfake Detection](https://mlsanity.com/paper/2606.25328/) (2026)
- [DAMOS: Learning Distortion-Aware Speech Quality Assessment through Explicit Distortion Localization](https://mlsanity.com/paper/2608.21176/) (2026)

## Abstract (arXiv)

The task of partially spoofed audio localization aims to accurately determine audio authenticity at a frame level. Although some works have achieved encouraging results, utilizing boundary information within a single model remains an unexplored research topic. In this work, we propose a novel method called Boundary-aware Attention Mechanism (BAM). Specifically, it consists of two core modules: Boundary Enhancement and Boundary Frame-wise Attention. The former assembles the intra-frame and inter-frame information to extract discriminative boundary features that are subsequently used for boundary position detection and authenticity decision, while the latter leverages boundary prediction results to explicitly control the feature interaction between frames, which achieves effective discrimination between real and fake frames. Experimental results on PartialSpoof database demonstrate our proposed method achieves the best performance. The code is available at https://github.com/media-sec-lab/BAM.