论文标题
融合全球和局部特征，用于广义AI合成图像检测
Fusing Global and Local Features for Generalized AI-Synthesized Image Detection
论文作者
论文摘要
随着生成对抗网络（GAN）和深击的发展，AI合成的图像现在具有如此高质量，以至于人类几乎无法将它们与真实图像区分开。媒体取证必须开发检测器以准确暴露它们。现有的检测方法在生成的图像检测中显示出高性能，但是它们在现实情况下趋于概括，在现实情况下，合成图像通常使用未知的源数据生成不见的模型。在这项工作中，我们强调了将整个图像中信息组合在一起的重要性，以及提高AI合成图像检测的概括能力方面的信息。具体而言，我们设计了一个两分支模型，以结合整个图像中的全局空间信息和新型补丁选择模块选择的多个贴片的局部信息特征。多头注意机制进一步用于融合全球和本地特征。我们收集了由19个模型合成的高度多样化的数据集，这些模型具有各种对象和决议来评估我们的模型。实验结果证明了我们方法检测生成图像的高精度和良好的概括能力。我们的代码可在https://github.com/littlejuyan/fusingglobalandlocal上找到。
With the development of the Generative Adversarial Networks (GANs) and DeepFakes, AI-synthesized images are now of such high quality that humans can hardly distinguish them from real images. It is imperative for media forensics to develop detectors to expose them accurately. Existing detection methods have shown high performance in generated images detection, but they tend to generalize poorly in the real-world scenarios, where the synthetic images are usually generated with unseen models using unknown source data. In this work, we emphasize the importance of combining information from the whole image and informative patches in improving the generalization ability of AI-synthesized image detection. Specifically, we design a two-branch model to combine global spatial information from the whole image and local informative features from multiple patches selected by a novel patch selection module. Multi-head attention mechanism is further utilized to fuse the global and local features. We collect a highly diverse dataset synthesized by 19 models with various objects and resolutions to evaluate our model. Experimental results demonstrate the high accuracy and good generalization ability of our method in detecting generated images. Our code is available at https://github.com/littlejuyan/FusingGlobalandLocal.
