  This paper investigates the impact of different standard environmental sound
representations (spectrograms) on the recognition performance and adversarial
attack robustness of a victim residual convolutional neural network, namely
ResNet-18. Our main motivation for focusing on such a front-end classifier
rather than other complex architectures is balancing recognition accuracy and
the total number of training parameters. Herein, we measure the impact of
different settings required for generating more informative Mel-frequency
cepstral coefficient (MFCC), short-time Fourier transform (STFT), and discrete
wavelet transform (DWT) representations on our front-end model. This
measurement involves comparing the classification performance over the
adversarial robustness. We demonstrate an inverse relationship between
recognition accuracy and model robustness against six benchmarking attack
algorithms on the balance of average budgets allocated by the adversary and the
attack cost. Moreover, our experimental results have shown that while the
ResNet-18 model trained on DWT spectrograms achieves a high recognition
accuracy, attacking this model is relatively more costly for the adversary than
other 2D representations. We also report some results on different
convolutional neural network architectures such as ResNet-34, ResNet-56,
AlexNet, and GoogLeNet, SB-CNN, and LSTM-based.

Sound;Cryptography and Security;Computer Vision and Pattern Recognition;Machine Learning;Audio and Speech Processing