# Benchmarking Audio Deepfake Detection Robustness in Real-world Communication Scenarios

arXiv: https://arxiv.org/abs/2504.12423 · HTML: https://mlsanity.com/paper/2504.12423/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2025.

## Built on by (most cited first, 3 of 3)

- [Codec-Robust Attacks on Audio LLMs](https://mlsanity.com/paper/2605.20519/) (2026)
- [Teffic-Audio: Tell Fact from Fiction](https://mlsanity.com/paper/2607.28351/) (2026)
- [Proteus: Automated Adversarial Robustness Testing for Audio Deepfake Detectors](https://mlsanity.com/paper/2606.29544/) (2026)

## Abstract (arXiv)

Existing Audio Deepfake Detection (ADD) systems often struggle to generalise effectively due to the significantly degraded audio quality caused by audio codec compression and channel transmission effects in real-world communication scenarios. To address this challenge, we developed a rigorous benchmark to evaluate the performance of the ADD system under such scenarios. We introduced ADD-C, a new test dataset to evaluate the robustness of ADD systems under diverse communication conditions, including different combinations of audio codecs for compression and packet loss rates. Benchmarking three baseline ADD models on the ADD-C dataset demonstrated a significant decline in robustness under such conditions. A novel Data Augmentation (DA) strategy was proposed to improve the robustness of ADD systems. Experimental results demonstrated that the proposed approach significantly enhances the performance of ADD systems on the proposed ADD-C dataset. Our benchmark can assist future efforts towards building practical and robustly generalisable ADD systems.