论文标题
在实际情况下检测CNN生成的面部图像
Detecting CNN-Generated Facial Images in Real-World Scenarios
论文作者
论文摘要
人工，CNN生成的图像现在具有如此高质量，以至于人类难以区分它们与真实图像。已经提出了几种算法检测方法，但是这些方法似乎对来自未知来源的数据概括，使其对于实际情况而言是不可行的。在这项工作中，我们提出了一个框架，用于在现实世界中评估检测方法，该框架由跨模型，交叉数据和后处理评估组成，并使用建议的框架评估最先进的检测方法。此外，我们研究了常用图像预处理方法的有用性。最后，我们通过进行在线调查来评估人类在检测CNN生成的图像以及影响这种绩效的因素时评估人类的绩效。我们的结果表明，基于CNN的检测方法还不够强大，无法在现实世界中使用。
Artificial, CNN-generated images are now of such high quality that humans have trouble distinguishing them from real images. Several algorithmic detection methods have been proposed, but these appear to generalize poorly to data from unknown sources, making them infeasible for real-world scenarios. In this work, we present a framework for evaluating detection methods under real-world conditions, consisting of cross-model, cross-data, and post-processing evaluation, and we evaluate state-of-the-art detection methods using the proposed framework. Furthermore, we examine the usefulness of commonly used image pre-processing methods. Lastly, we evaluate human performance on detecting CNN-generated images, along with factors that influence this performance, by conducting an online survey. Our results suggest that CNN-based detection methods are not yet robust enough to be used in real-world scenarios.
