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Robust audio anti-spoofing has been increasingly challenging due to therecent advancements on deepfake techniques. While spectrograms havedemonstrated their capability for anti-spoofing, complementary informationpresented in multi-order spectral patterns have not been well explored, whichlimits their effectiveness for varying spoofing attacks. Therefore, we proposea novel deep learning method with a spectral fusion-reconstruction strategy,namely S2pecNet, to utilise multi-order spectral patterns for robust audioanti-spoofing representations. Specifically, spectral patterns up tosecond-order are fused in a coarse-to-fine manner and two branches are designedfor the fine-level fusion from the spectral and temporal contexts. Areconstruction from the fused representation to the input spectrograms furtherreduces the potential fused information loss. Our method achieved thestate-of-the-art performance with an EER of 0.77% on a widely used dataset:ASVspoof2019 LA Challenge.

Robust Audio Anti-Spoofing with Fusion-Reconstruction Learning on Multi-Order Spectrograms