The Perceived Fragility of Explanations in Audio Models: Manipulation of Attribution with Unchanged Predictions
This paper investigates the fragility of post-hoc explanation methods in audio deepfake detection. While previous work on explanation manipulation focused on images using standard $L_p$ metrics, we introduce a psychoacoustic framework that optimizes inaudible perturbations to decouple model attributions from final classifications. We evaluate this vulnerability across state-of-the-art architectures under strict prediction-preserving constraints. By evaluating the manipulation cost through domain-specific perceptual audio quality metrics alongside explanation alignment criteria, our framework demonstrates that an adversary can systematically distort automated explanation heatmaps while pre...
| Authors | Piotr Kitłowski, Dominik Wiącek, Mateusz Modrzejewski |
|---|---|
| Primary category | cs.SD |
| Published | 12 June 2026 |
| Last updated at source | 12 June 2026 |
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