# Weakly-supervised Audio Temporal Forgery Localization via Progressive Audio-language Co-learning Network

arXiv: https://arxiv.org/abs/2505.01880 · HTML: https://mlsanity.com/paper/2505.01880/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 29 references that are in the map.
- First posted on arXiv 3 May 2025.

## Builds on (most cited first, 23 of 23)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching](https://mlsanity.com/paper/2410.06885/) (2024)
- [AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks](https://mlsanity.com/paper/2110.01200/) (2021)
- [E2 TTS: Embarrassingly Easy Fully Non-Autoregressive Zero-Shot TTS](https://mlsanity.com/paper/2406.18009/) (2024)
- [Automatic speaker verification spoofing and deepfake detection using wav2vec 2.0 and data augmentation](https://mlsanity.com/paper/2202.12233/) (2022)
- [AV-Deepfake1M: A Large-Scale LLM-Driven Audio-Visual Deepfake Dataset](https://mlsanity.com/paper/2311.15308/) (2023)
- [CoLA: Weakly-Supervised Temporal Action Localization with Snippet Contrastive Learning](https://mlsanity.com/paper/2103.16392/) (2021)
- [A Comparative Study on Recent Neural Spoofing Countermeasures for Synthetic Speech Detection](https://mlsanity.com/paper/2103.11326/) (2021)
- [Beyond the Prior Forgery Knowledge: Mining Critical Clues for General Face Forgery Detection](https://mlsanity.com/paper/2304.12489/) (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)
- [Glitch in the Matrix: A Large Scale Benchmark for Content Driven Audio-Visual Forgery Detection and Localization](https://mlsanity.com/paper/2305.01979/) (2023)
- [Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly Detection](https://mlsanity.com/paper/2306.14451/) (2023)
- [UMMAFormer: A Universal Multimodal-adaptive Transformer Framework for Temporal Forgery Localization](https://mlsanity.com/paper/2308.14395/) (2023)
- [Pixel-Inconsistency Modeling for Image Manipulation Localization](https://mlsanity.com/paper/2310.00234/) (2023)
- [MuseTalk: Real-Time High-Fidelity Video Dubbing via Spatio-Temporal Sampling](https://mlsanity.com/paper/2410.10122/) (2024)
- [GROOT: Generating Robust Watermark for Diffusion-Model-Based Audio Synthesis](https://mlsanity.com/paper/2407.10471/) (2024)
- [Proposal-Based Multiple Instance Learning for Weakly-Supervised Temporal Action Localization](https://mlsanity.com/paper/2305.17861/) (2023)
- [An Efficient Temporary Deepfake Location Approach Based Embeddings for Partially Spoofed Audio Detection](https://mlsanity.com/paper/2309.03036/) (2023)
- [Coarse-to-Fine Proposal Refinement Framework for Audio Temporal Forgery Detection and Localization](https://mlsanity.com/paper/2407.16554/) (2024)
- [Revisiting Foreground and Background Separation in Weakly-supervised Temporal Action Localization: A Clustering-based Approach](https://mlsanity.com/paper/2312.14138/) (2023)
- [Full-Stage Pseudo Label Quality Enhancement for Weakly-supervised Temporal Action Localization](https://mlsanity.com/paper/2407.08971/) (2024)

## Built on by (most cited first, 6 of 6)

- [A Multimodal Deviation Perceiving Framework for Weakly-Supervised Temporal Forgery Localization](https://mlsanity.com/paper/2507.16596/) (2025)
- [Weakly-Supervised Image Forgery Localization via Vision-Language Collaborative Reasoning Framework](https://mlsanity.com/paper/2508.01338/) (2025)
- [Weakly Supervised Multimodal Temporal Forgery Localization via Multitask Learning](https://mlsanity.com/paper/2508.02179/) (2025)
- [Safeguarding Facial Identity against Diffusion-based Face Swapping via Cascading Pathway Disruption](https://mlsanity.com/paper/2601.14738/) (2026)
- [CIEC: Coupling Implicit and Explicit Cues for Multimodal Weakly Supervised Manipulation Localization](https://mlsanity.com/paper/2602.02175/) (2026)
- [GEM-TFL: Bridging Weak and Full Supervision for Forgery Localization through EM-Guided Decomposition and Temporal Refinement](https://mlsanity.com/paper/2603.05095/) (2026)

## Abstract (arXiv)

Audio temporal forgery localization (ATFL) aims to find the precise forgery regions of the partial spoof audio that is purposefully modified. Existing ATFL methods rely on training efficient networks using fine-grained annotations, which are obtained costly and challenging in real-world scenarios. To meet this challenge, in this paper, we propose a progressive audio-language co-learning network (LOCO) that adopts co-learning and self-supervision manners to prompt localization performance under weak supervision scenarios. Specifically, an audio-language co-learning module is first designed to capture forgery consensus features by aligning semantics from temporal and global perspectives. In this module, forgery-aware prompts are constructed by using utterance-level annotations together with learnable prompts, which can incorporate semantic priors into temporal content features dynamically. In addition, a forgery localization module is applied to produce forgery proposals based on fused forgery-class activation sequences. Finally, a progressive refinement strategy is introduced to generate pseudo frame-level labels and leverage supervised semantic contrastive learning to amplify the semantic distinction between real and fake content, thereby continuously optimizing forgery-aware features. Extensive experiments show that the proposed LOCO achieves SOTA performance on three public benchmarks.