%20is%20critical%20due%20to%20the%20emergence%20of%20new%20spoofing%20techniques.%20Traditional%20FAD%20methods%20often%20focus%20solely%20on%20distinguishing%20between%20genuine%20and%20known%20spoofed%20audio.%20We%20propose%20a%20Genuine-Focused%20Learning%20(GFL)%20framework%20guided%2C%20aiming%20for%20highly%20generalized%20FAD%2C%20called%20GFL-FAD.%20This%20method%20incorporates%20a%20Counterfactual%20Reasoning%20Enhanced%20Representation%20(CRER)%20based%20on%20audio%20reconstruction%20using%20the%20Mask%20AutoEncoder%20(MAE)%20architecture%20to%20accurately%20model%20genuine%20audio%20features.%20To%20reduce%20the%20influence%20of%20spoofed%20audio%20during%20training%2C%20we%20introduce%20a%20genuine%20audio%20reconstruction%20loss%2C%20maintaining%20the%20focus%20on%20learning%20genuine%20data%20features.%20In%20addition%2C%20content-related%20bottleneck%20(BN)%20features%20are%20extracted%20from%20the%20MAE%20to%20supplement%20the%20knowledge%20of%20the%20original%20audio.%20These%20BN%20features%20are%20adaptively%20fused%20with%20CRER%20to%20further%20improve%20robustness.%20Our%20method%20achieves%20state-of-the-art%20performance%20with%20an%20EER%20of%200.25%25%20on%20ASVspoof2019%20LA.)
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