An Intervention-Based Framework for Shortcut Diagnosis in Spoofing Countermeasures

Santiago Rubio; Pilar Bello; Dayana Ribas; Antonio Miguel; Eduardo Lleida; Alfonso Ortega

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arXiv · 2607.03150

An Intervention-Based Framework for Shortcut Diagnosis in Spoofing Countermeasures

Santiago Rubio·Pilar Bello·Dayana Ribas·Antonio Miguel·Eduardo Lleida·Alfonso Ortega

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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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Santiago Rubio, Pilar Bello, Dayana Ribas, Antonio Miguel, Eduardo Lleida, Alfonso Ortega. 2026-07-03. An Intervention-Based Framework for Shortcut Diagnosis in Spoofing Countermeasures. https://arxiv.org/abs/2607.03150

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