Abstract
Recent advances in generative AI have led to the development of techniques to generate visually realistic synthetic video. While a number of techniques have been developed to detect AI-generated synthetic images, in this paper we show that synthetic image detectors are unable to detect synthetic videos. We demonstrate that this is because synthetic video generators introduce substantially different traces than those left by image generators. Despite this, we show that synthetic video traces can be learned, and used to perform reliable synthetic video detection or generator source attribution even after H.264 re-compression. Furthermore, we demonstrate that while detecting videos from new generators through zero-shot transferability is challenging, accurate detection of videos from a new generator can be achieved through few-shot learning.
Abstract (translated)
近年来，在生成式人工智能（Generative AI）方面的进步导致了生成视觉上逼真的合成视频的技术的发展。虽然已经开发了许多方法来检测由AI生成的合成图像，但在本文中，我们证明了合成图像检测器无法检测合成视频。我们证明了这是因为合成视频生成器引入了与图像生成器留下的痕迹显著不同的迹线。尽管如此，我们证明了合成视频痕迹可以学习，并用于可靠的合成视频检测或生成器来源 attribution，即使在H.264重新压缩之后。此外，我们证明了从零散转移学习中检测新生成器生成的视频是具有挑战性的，但通过几散学习可以准确地从新生成器中检测到视频。
URL
https://arxiv.org/abs/2404.15955
