SONAR: Spectral‑Contrastive Audio Residuals for Generalizable Deepfake Detection

Deepfake audio detectors often fail to generalize to unseen attacks, in part due to spectral bias: neural networks prioritize low-frequency structure while under-exploiting subtle high-frequency (HF) artifacts left by generative models. We introduce SONAR (Spectral-cONtrastive Audio Residuals), a frequency-guided framework that explicitly enforces representation-level consistency between semantic content and HF residuals. Unlike prior frequency-aware or dual-stream detectors that treat HF cues as auxiliary features, SONAR encourages structured interaction between content and noise representations in latent space. The model employs a dual-path architecture in which an XLSR encoder captures low-frequency content, while a parallel branch with learnable, value-constrained 1D SRM (Spatial Rich Model) high-pass filters distills HF residuals. The two representations are fused via frequency cross-attention and trained with a Jensen--Shannon alignment loss that promotes LF–HF consistency for genuine audio and amplifies inconsistency for deepfakes. Evaluated on ASVspoof 2021 and in-the-wild benchmarks, SONAR achieves state-of-the-art performance in a single run setting and converges faster than strong baselines. By mitigating the effects of spectral bias through frequency-guided alignment, SONAR provides a fully data-driven and architecture-agnostic approach to generalizable audio deepfake detection.