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This technical report describes Chung-Ang University and Korea University(CAU_KU) team's model participating in the Audio Deep Synthesis Detection (ADD)2022 Challenge, track 1: Low-quality fake audio detection. For track 1, wepropose a frequency feature masking (FFM) augmentation technique to deal with alow-quality audio environment. %detection that spectrogram-based models can beapplied. We applied FFM and mixup augmentation on five spectrogram-based deepneural network architectures that performed well for spoofing detection usingmel-spectrogram and constant Q transform (CQT) features. Our best submissionachieved 23.8% of EER ranked 3rd on track 1.

CAU_KU team's submission to ADD 2022 Challenge task 1: Low-quality fake audio detection through frequency feature masking