论文标题
gan产生假脸检测
One-Shot GAN Generated Fake Face Detection
论文作者
论文摘要
对于智能系统而言，伪造的面部检测是一个重大挑战，因为生成模型每天都变得更加强大。随着假面的质量的提高，训练有素的模型变得越来越低效率地检测出新颖的假面，因为相应的训练数据被认为已过时。在这种情况下，强大的一声学习方法与可变培训数据的要求更兼容。在本文中，我们提出了一种通用的单发gan产生的假面检测方法，该方法可用于明显不同的异常检测区域。所提出的方法是基于通过场景理解模型从面上提取外面的对象。为此，我们使用艺术现场的理解和对象检测方法作为一种预处理工具来检测面部的怪异对象。其次，我们根据所有培训数据，鉴于所有检测到的未检测到的对象，我们创建了一袋单词。这样，我们将每个图像转换为一个稀疏向量，其中每个特征代表与图像中每个检测到的对象相关的置信分数。我们的实验表明，我们可以根据外观外观功能将假面与真实面孔区分开。这意味着，当我们通过场景理解和对象检测模型分析它们时，在假面中检测到不同的对象集。我们证明，根据我们对风格生成的假面的实验，提出的方法可以胜过以前的方法。
Fake face detection is a significant challenge for intelligent systems as generative models become more powerful every single day. As the quality of fake faces increases, the trained models become more and more inefficient to detect the novel fake faces, since the corresponding training data is considered outdated. In this case, robust One-Shot learning methods is more compatible with the requirements of changeable training data. In this paper, we propose a universal One-Shot GAN generated fake face detection method which can be used in significantly different areas of anomaly detection. The proposed method is based on extracting out-of-context objects from faces via scene understanding models. To do so, we use state of the art scene understanding and object detection methods as a pre-processing tool to detect the weird objects in the face. Second, we create a bag of words given all the detected out-of-context objects per all training data. This way, we transform each image into a sparse vector where each feature represents the confidence score related to each detected object in the image. Our experiments show that, we can discriminate fake faces from real ones in terms of out-of-context features. It means that, different sets of objects are detected in fake faces comparing to real ones when we analyze them with scene understanding and object detection models. We prove that, the proposed method can outperform previous methods based on our experiments on Style-GAN generated fake faces.
