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.

翻译：深层次的学习算法正在迅速改变视听媒体的生成方式。 深层次的学习所产生的合成视听媒体 — — 通常被统称为“深假” — — 具有许多令人印象深刻的特点;它们越来越容易产生,并且可能无法与通过传感器记录的真实声音和图像区分开来。 对这一技术发展引起的伦理问题给予了极大的关注。 在这里,我把重点放在一系列与合成视听媒体概念有关的问题上,它在更广泛的视听媒体分类中的位置,以及深层次的学习技巧与较传统的媒体合成方法有何不同。 在审查了用于媒体操作和生成的深层次学习管道的重要伦理特征之后,我争论说,“深假”和用这种管道制作的相关合成媒体不仅比以往的方法提供了渐进的改进,而且挑战了传统分类学的区分,并为真正新型的音像媒体铺平了道路。