  Deep learning algorithms are rapidly changing the way in which audiovisual
media can be produced. Synthetic audiovisual media generated with deep learning
- often subsumed colloquially under the label "deepfakes" - have a number of
impressive characteristics; they are increasingly trivial to produce, and can
be indistinguishable from real sounds and images recorded with a sensor. Much
attention has been dedicated to ethical concerns raised by this technological
development. Here, I focus instead on a set of issues related to the notion of
synthetic audiovisual media, its place within a broader taxonomy of audiovisual
media, and how deep learning techniques differ from more traditional approaches
to media synthesis. After reviewing important etiological features of deep
learning pipelines for media manipulation and generation, I argue that
"deepfakes" and related synthetic media produced with such pipelines do not
merely offer incremental improvements over previous methods, but challenge
traditional taxonomical distinctions, and pave the way for genuinely novel
kinds of audiovisual media.
