SRAM: Learning Spectral Relation-Aware Representations for Environmental Sound Deepfake Detection

Environmental sound deepfake detection (ESDD) models are critical for countering fabricated environmental audio, which can mislead users of audio-based assistive technologies and trigger unintended smart-home responses. A reliable ESDD method requires accurate detection even when the source datasets and generators are unseen in training. Existing ESDD methods broadly follow two paradigms: task-specific discriminative models and generative methods based on audio large language models (ALLMs). We find that both approaches have significant limitations: (i) discriminative methods are highly dependent on the seen domain conditions of the training data, and their classification performance drops significantly when the test audio comes from unseen domains; (ii) generative methods generally underperform and incur higher per-sample inference latency and deployment costs. We conduct a large-scale evaluation of 15 ALLMs, spanning five model families, on the ESDD task. The results show that their zero-shot performance is close to random guessing, and unlike the gains reported in speech deepfake detection, their performance remains significantly inferior to that of strong task-specific discriminative models even after fine-tuning. Further analysis shows that a classifier built on an ALLM's audio encoder can outperform the full generative model, while local spectro-temporal residuals and time-frequency geometry reveal differences between real and AI-generated audio. Motivated by these findings, we propose a novel detection framework, called Spectral-Relational Artifact Modeling (SRAM). SRAM combines audio representation learning with spectral relation modeling. It builds on the Qwen3-Omni audio encoder, using cross-layer trajectory refinement to integrate information from multiple encoder depths and sparse evidence pooling to focus on informative temporal tokens. To complement these audio representations, we use a spectrogram-residual branch to capture local spectral differences and a structure-tensor branch to describe local time-frequency geometry. Extensive experiments demonstrate SRAM’s strong detection performance, generalization to unseen domains, and robustness to unseen perturbations.