An Intervention-Based Framework for Shortcut Diagnosis in Spoofing Countermeasures

2026-07-03

AI Summary

This study addresses the performance degradation of deepfake audio detection models in real-world scenarios, often caused by reliance on dataset-specific shortcut features such as non-speech artifacts. The work introduces causal intervention into anti-spoofing diagnostics for the first time, proposing a directed graphical model-based intervention framework that formally distinguishes between shortcut learning and legitimate domain shifts. Through controlled acoustic perturbations—targeting non-speech segments, spectral characteristics, and energy profiles—and corpus-level distributional analysis, the authors systematically evaluate model sensitivity. Experiments on the ASVspoof dataset reveal that interventions on non-speech intervals lead to significant performance drops, identifying them as critical shortcut features and providing a clear direction for enhancing model robustness.

Abstract

While deepfake audio detection systems achieve high performance in controlled benchmarks, their reliability often diminishes in the wild. Prior work shows that dataset-specific artifacts contribute to this gap. Yet, systematic tools to identify which acoustic properties a model exploits as shortcuts remain limited. We propose an intervention-based diagnostic framework, grounded in a directed graphical model, that formally distinguishes confound-driven shortcut dependencies from legitimate domain shift. We operationalise this through controlled acoustic perturbations targeting non-speech structure, spectral content, and signal energy, complemented by corpus-level distributional analysis. Evaluating XLS-R-300M with RawGAT-ST across ASVspoof challenges datasets, we quantify model sensitivity to specific intervention types. Results reveal that non-speech interventions produce the largest performance shifts, confirming non-speech intervals as a dominant shortcut.

Problem

• shortcut learning
• deepfake audio detection
• dataset artifacts
• acoustic properties
• model reliability

Innovation

• intervention-based diagnosis
• shortcut learning
• spoofing countermeasures
• acoustic perturbations
• confound-driven dependencies