论文标题
深度学习和合成媒体
Deep Learning and Synthetic Media
论文作者
论文摘要
深度学习算法正在迅速改变产生视听媒体的方式。由深度学习产生的合成视听媒体 - 通常被俗称“深击”的含量 - 具有许多令人印象深刻的特征；它们的生产越来越小，与传感器录制的真实声音和图像无法区分。这项技术发展引起的道德问题已经非常关注。在这里，我专注于与合成视听媒体的概念有关的一系列问题，其位置在更广泛的视听媒体分类法中以及深度学习技术与更传统的媒体合成方法有何不同。在审查了深度学习管道的重要病因和代媒体操纵和代代相传之后，我认为使用此类管道生产的“深层”和相关的合成媒体不仅可以对以前的方法提供增量的改进，而且还挑战了传统的分类学区别，并为真正的新颖听觉媒体铺平了道路。
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.
