# Interpretable Temporal Class Activation Representation for Audio Spoofing Detection

arXiv: https://arxiv.org/abs/2406.08825 · HTML: https://mlsanity.com/paper/2406.08825/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2024.

## Built on by (most cited first, 4 of 4)

- [Multilingual Source Tracing of Speech Deepfakes: A First Benchmark](https://mlsanity.com/paper/2508.04143/) (2025)
- [What Does an Audio Deepfake Detector Focus on? A Study in the Time Domain](https://mlsanity.com/paper/2501.13887/) (2025)
- [A Data-Driven Diffusion-based Approach for Audio Deepfake Explanations](https://mlsanity.com/paper/2506.03425/) (2025)
- [What Survives the Codec Shift: Pooled No-Vocals Residuals for Speech Deepfake Detection](https://mlsanity.com/paper/2609.33375/) (2026)

## Abstract (arXiv)

Explaining the decisions made by audio spoofing detection models is crucial for fostering trust in detection outcomes. However, current research on the interpretability of detection models is limited to applying XAI tools to post-trained models. In this paper, we utilize the wav2vec 2.0 model and attentive utterance-level features to integrate interpretability directly into the model's architecture, thereby enhancing transparency of the decision-making process. Specifically, we propose a class activation representation to localize the discriminative frames contributing to detection. Furthermore, we demonstrate that multi-label training based on spoofing types, rather than binary labels as bonafide and spoofed, enables the model to learn distinct characteristics of different attacks, significantly improving detection performance. Our model achieves state-of-the-art results, with an EER of 0.51% and a min t-DCF of 0.0165 on the ASVspoof2019-LA set.