Cross-representation transferability of adversarial perturbations: From spectrograms to audio waveforms
Cross-representation transferability of adversarial perturbations: From spectrograms to audio waveforms
Koerich, Karl M. and Esmailpour, Mohammad and Abdoli, Sajjad and Britto, Alceu S. and Koerich, Alessandro L.
arXiv 2019
Abstract : This paper shows the susceptibility of spectrogram-based audio classifiers to adversarial attacks and the transferability of such attacks to audio waveforms. Some commonly adversarial attacks to images have been applied to Mel-frequency and short-time Fourier transform spectrograms and such perturbed spectrograms are able to fool a 2D convolutional neural network (CNN) for music genre classification with a high fooling rate and high confidence. Such attacks produce perturbed spectrograms that are visually imperceptible by humans. Experimental results on a dataset of western music have shown that the 2D CNN achieves up to 81.87% of mean accuracy on legitimate examples and such a performance drops to 12.09% on adversarial examples. Furthermore, the audio signals reconstructed from the adversarial spectrograms produce audio waveforms that perceptually resemble the legitimate audio.
