[2607.17761] Time-Frequency Consistency Learning for Robust Speech Deepfake Detection

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arXiv:2607.17761 (cs)

[Submitted on 20 Jul 2026]

Title:Time-Frequency Consistency Learning for Robust Speech Deepfake Detection

Authors:Jun Xue, Zhuolin Yi, Yanzhen Ren, Yihuan Huang, Jiayu Xiong, Yi Chai, Guanxiang Feng, Jiajun Liu, Tong Zhang<br>View a PDF of the paper titled Time-Frequency Consistency Learning for Robust Speech Deepfake Detection, by Jun Xue and 8 other authors

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Abstract:Recently, speech deepfake detection (SDD) has achieved significant progress. However, its robustness evaluation remains largely confined to controlled additive noise scenarios, lacking systematic investigation of the complex distortions introduced by acoustic front-end (AFE) processing pipelines in real-world deployments. In this work, we simulate a unified AFE pipeline comprising acoustic echo cancellation, noise suppression, automatic gain control, and voice activity detection (VAD), and conduct a comprehensive evaluation of current state-of-the-art models. The results show that the nonlinear and time-frequency coupled distortions introduced by AFE significantly degrade detection performance. To address this issue, we propose a Time-Frequency Consistency Learning (TFCL) framework, which aims to learn invariant spoofing representations that remain stable before and after AFE processing. We observe that AFE not only introduces temporal misalignment (e.g., segment-level shifts caused by VAD), but also weakens or distorts critical frequency-domain cues. To this end, TFCL employs an attention-driven soft alignment mechanism to capture cross-temporal dependencies, along with frequency-domain structural consistency constraints to enforce feature invariance. As a result, the model is able to maintain stable representations under both temporal perturbations and spectral distortions. Extensive experimental results demonstrate that the proposed method effectively mitigates the performance degradation caused by AFE processing, significantly improving the robustness of SDD in real-world scenarios. The code is available at this https URL.

Comments:<br>Accepted by ACM MM 2026

Subjects:

Sound (cs.SD); Artificial Intelligence (cs.AI)

Cite as:<br>arXiv:2607.17761 [cs.SD]

(or<br>arXiv:2607.17761v1 [cs.SD] for this version)

https://doi.org/10.48550/arXiv.2607.17761

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Jun Xue [view email]<br>[v1]<br>Mon, 20 Jul 2026 09:51:03 UTC (7,470 KB)

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