# CompSpoof: A Dataset and Joint Learning Framework for Component-Level Audio Anti-spoofing Countermeasures

arXiv: https://arxiv.org/abs/2509.15804 · HTML: https://mlsanity.com/paper/2509.15804/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2025.

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

- [SynSFX: Multi-Model Sound Effects Synthesis Dataset for Deepfake Detection and Evaluation](https://mlsanity.com/paper/2607.04848/) (2026)
- [PC-Mix: Partial-Component Audio Spoofing Detection under Mixed Speech and Environmental Sound Conditions](https://mlsanity.com/paper/2607.10345/) (2026)
- [MADBench: A Benchmark for Modality-Aware Audio Deepfake Detection](https://mlsanity.com/paper/2608.09593/) (2026)
- [ToolDF: Tool-Integrated Reasoning for Mixed-Authenticity Audio Deepfake Detection](https://mlsanity.com/paper/2609.03620/) (2026)
- [What Survives the Codec Shift: Pooled No-Vocals Residuals for Speech Deepfake Detection](https://mlsanity.com/paper/2609.33375/) (2026)

## Abstract (arXiv)

Component-level audio Spoofing (Comp-Spoof) targets a new form of audio manipulation where only specific components of a signal, such as speech or environmental sound, are forged or substituted while other components remain genuine. Existing anti-spoofing datasets and methods treat an utterance or a segment as entirely bona fide or entirely spoofed, and thus cannot accurately detect component-level spoofing. To address this, we construct a new dataset, CompSpoof, covering multiple combinations of bona fide and spoofed speech and environmental sound. We further propose a separation-enhanced joint learning framework that separates audio components apart and applies anti-spoofing models to each one. Joint learning is employed, preserving information relevant for detection. Extensive experiments demonstrate that our method outperforms the baseline, highlighting the necessity of separate components and the importance of detecting spoofing for each component separately. Datasets and code are available at: https://github.com/XuepingZhang/CompSpoof.