  Automatic Speaker Verification systems are gaining popularity these days;
spoofing attacks are of prime concern as they make these systems vulnerable.
Some spoofing attacks like Replay attacks are easier to implement but are very
hard to detect thus creating the need for suitable countermeasures. In this
paper, we propose a speech classifier based on deep-convolutional neural
network to detect spoofing attacks. Our proposed methodology uses acoustic
time-frequency representation of power spectral densities on Mel frequency
scale (Mel-spectrogram), via deep residual learning (an adaptation of ResNet-34
architecture). Using a single model system, we have achieved an equal error
rate (EER) of 0.9056% on the development and 5.32% on the evaluation dataset of
logical access scenario and an equal error rate (EER) of 5.87% on the
development and 5.74% on the evaluation dataset of physical access scenario of
ASVspoof 2019.
