An Intervention-Based Framework for Shortcut Diagnosis in Spoofing Countermeasures

• URL: http://arxiv.org/abs/2607.03150v1
• Date: Fri, 03 Jul 2026 09:42:31 GMT
• Title: An Intervention-Based Framework for Shortcut Diagnosis in Spoofing Countermeasures
• Abstract summary: We propose an intervention-based diagnostic framework to identify confound-driven shortcut dependencies.<n>We operationalise this through controlled acoustic perturbations targeting non-speech structure, spectral content, and signal energy.<n>Results reveal that non-speech interventions produce the largest performance shifts, confirming non-speech intervals as a dominant shortcut.
• Score: 5.593982292458519
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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