  Attackers may manipulate audio with the intent of presenting falsified
reports, changing an opinion of a public figure, and winning influence and
power. The prevalence of inauthentic multimedia continues to rise, so it is
imperative to develop a set of tools that determines the legitimacy of media.
We present a method that analyzes audio signals to determine whether they
contain real human voices or fake human voices (i.e., voices generated by
neural acoustic and waveform models). Instead of analyzing the audio signals
directly, the proposed approach converts the audio signals into spectrogram
images displaying frequency, intensity, and temporal content and evaluates them
with a Convolutional Neural Network (CNN). Trained on both genuine human voice
signals and synthesized voice signals, we show our approach achieves high
accuracy on this classification task.
