# A Preliminary Case Study on Long-Form In-the-Wild Audio Spoofing Detection

arXiv: https://arxiv.org/abs/2408.14066 · HTML: https://mlsanity.com/paper/2408.14066/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2024.

## Built on by (most cited first, 1 of 1)

- [From Scores to Evidence: Auditable Decisions Can Improve Speech Deepfake Detection](https://mlsanity.com/paper/2609.08899/) (2026)

## Abstract (arXiv)

Audio spoofing detection has become increasingly important due to the rise in real-world cases. Current spoofing detectors, referred to as spoofing countermeasures (CM), are mainly trained and focused on audio waveforms with a single speaker and short duration. This study explores spoofing detection in more realistic scenarios, where the audio is long in duration and features multiple speakers and complex acoustic conditions. We test the widely-acquired AASIST under this challenging scenario, looking at the impact of multiple variations such as duration, speaker presence, and acoustic complexities on CM performance. Our work reveals key issues with current methods and suggests preliminary ways to improve them. We aim to make spoofing detection more applicable in more in-the-wild scenarios. This research is served as an important step towards developing detection systems that can handle the challenges of audio spoofing in real-world applications.