Abstract
In recent years, the emergence of models capable of generating images from text has attracted considerable interest, offering the possibility of creating realistic images from text descriptions. Yet these advances have also raised concerns about the potential misuse of these images, including the creation of misleading content such as fake news and propaganda. This study investigates the effectiveness of using advanced vision-language models (VLMs) for synthetic image identification. Specifically, the focus is on tuning state-of-the-art image captioning models for synthetic image detection. By harnessing the robust understanding capabilities of large VLMs, the aim is to distinguish authentic images from synthetic images produced by diffusion-based models. This study contributes to the advancement of synthetic image detection by exploiting the capabilities of visual language models such as BLIP-2 and ViTGPT2. By tailoring image captioning models, we address the challenges associated with the potential misuse of synthetic images in real-world applications. Results described in this paper highlight the promising role of VLMs in the field of synthetic image detection, outperforming conventional image-based detection techniques. Code and models can be found at this https URL.
Abstract (translated)
近年来，能够从文本生成图像的模型的发展引起了相当大的关注，为从文本描述创建真实图像提供了可能。然而，这些进步也引发了对这些图像可能被滥用的高度关注，包括创建虚假新闻和宣传的内容。本研究旨在调查使用先进视觉语言模型（VLMs）进行合成图像识别的有效性。具体来说，重点是调整最先进的图像描述模型以进行合成图像检测。通过利用大型 VLMs 的稳健理解能力，目标是将真实图像与由扩散模型生成的合成图像区分开来。本研究为合成图像检测的发展做出了贡献，利用了视觉语言模型（如BLIP-2和ViTGPT2）的 capabilities。通过调整图像描述模型，我们解决了与合成图像在现实应用中可能被滥用的相关挑战。本文中描述的结果突出了VLMs在合成图像检测领域的前景，超过了基于图像的检测技术。代码和模型可在此链接找到：https://www.example.com/
URL
https://arxiv.org/abs/2404.02726
