  Deepfake is content or material that is generated or manipulated using AI
methods, to pass off as real. There are four different deepfake types: audio,
video, image and text. In this research we focus on audio deepfakes and how
people perceive it. There are several audio deepfake generation frameworks, but
we chose MelGAN which is a non-autoregressive and fast audio deepfake
generating framework, requiring fewer parameters. This study tries to assess
audio deepfake perceptions among college students from different majors. This
study also answers the question of how their background and major can affect
their perception towards AI generated deepfakes. We also analyzed the results
based on different aspects of: grade level, complexity of the grammar used in
the audio clips, length of the audio clips, those who knew the term deepfakes
and those who did not, as well as the political angle. It is interesting that
the results show when an audio clip has a political connotation, it can affect
what people think about whether it is real or fake, even if the content is
fairly similar. This study also explores the question of how background and
major can affect perception towards deepfakes.
