  State-of-the-art methods for audio generation suffer from fingerprint
artifacts and repeated inconsistencies across temporal and spectral domains.
Such artifacts could be well captured by the frequency domain analysis over the
spectrogram. Thus, we propose a novel use of long-range spectro-temporal
modulation feature -- 2D DCT over log-Mel spectrogram for the audio deepfake
detection. We show that this feature works better than log-Mel spectrogram,
CQCC, MFCC, etc., as a suitable candidate to capture such artifacts. We employ
spectrum augmentation and feature normalization to decrease overfitting and
bridge the gap between training and test dataset along with this novel feature
introduction. We developed a CNN-based baseline that achieved a 0.0849 t-DCF
and outperformed the best single system reported in the ASVspoof 2019
challenge. Finally, by combining our baseline with our proposed 2D DCT
spectro-temporal feature, we decrease the t-DCF score down by 14% to 0.0737,
making it one of the best systems for spoofing detection. Furthermore, we
evaluate our model using two external datasets, showing the proposed feature's
generalization ability. We also provide analysis and ablation studies for our
proposed feature and results.
