Hyper-realistic images that are not differentiable from authentic images to regular viewers have become extremely easy to generate and highly accessible. Furthermore, the increasing pervasiveness of social media networks in our daily lives has facilitated the easy dissemination of fake news accompanied by such synthetic images. Hyper-realistic artificial face images are often illicitly used as profile pictures on social media sites, further using such profiles to spread fabricated information, resulting in social perils. Most available synthetic image detectors are challenging to implement in practical scenarios due to their high complexity and performance degradation for images from Online Social Networks (OSNs). In this work, we develop a deep learning-based lightweight synthetic image detector called Relative Chrominance Distance Network (). In this paper, we introduce the RCD image feature set for the first time, which gives a pair-wise chrominance component-based distance measure. To show its effectiveness, we explore multiple luminance-chrominance spaces. Compared to the state-of-the-art (SOTA), our model hugely reduces the network parameter requirements, making it incredibly lightweight. We also study the robustness of the proposed solution against common post-processing operations in the context of online social media networks. Experimental results prove that the proposed solution achieves SOTA performance at a much lower complexity than available solutions.

中文翻译：

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少即是多：稳健的 GAN 生成人脸检测的极简方法

对于普通观众来说，超现实的图像与真实图像没有区别，它变得非常容易生成并且易于访问。此外，社交媒体网络在我们日常生活中的日益普及，使得伴随此类合成图像的假新闻很容易传播。超逼真的人造人脸图像经常被非法用作社交媒体网站上的个人资料图片，进一步利用此类个人资料传播捏造的信息，造成社会危害。大多数可用的合成图像检测器在实际场景中实现起来都具有挑战性，因为它们的复杂性很高，并且来自在线社交网络（OSN）的图像的性能下降。在这项工作中，我们开发了一种基于深度学习的轻量级合成图像检测器，称为相对色度距离网络（Relative Chrominance Distance Network）。在本文中，我们首次引入了 RCD 图像特征集，它给出了基于成对色度分量的距离度量。为了证明其有效性，我们探索了多个亮度-色度空间。与最先进的（SOTA）相比，我们的模型极大地降低了网络参数要求，使其极其轻量。我们还研究了所提出的解决方案针对在线社交媒体网络背景下常见后处理操作的稳健性。实验结果证明，所提出的解决方案以比现有解决方案低得多的复杂度实现了 SOTA 性能。