使用针对单个面部部分的CNN模型集合进行假图像检测

Akihisa Kawabe, Ryuto Haga, Yoichi Tomioka, Y. Okuyama, Jungpil Shin
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引用次数: 2

摘要

随着深度学习技术的快速发展,用人工智能(AI)创建人脸图像变得越来越容易。这些生成的图像接近于人类无法与真实图像区分的图像。为了避免虚假图像的滥用,有必要实现一种准确的检测方法。在本文中,我们提出了一种使用卷积神经网络(CNN)模型的集成模型的假图像检测,该模型专注于对单个面部部位的深度假检测。结果表明,基于不同人脸部位的深度假检测组合是有效的。这个想法可以在部分操纵的深度伪造图像/视频上采用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fake Image Detection Using An Ensemble of CNN Models Specialized For Individual Face Parts
With the rapid increase of deep learning technology, creating human face images with artificial intelligence (AI) is becoming easier. Those generated images are coming up to images that humans cannot distinguish from authentic ones. It is essential to realize an accurate method to detect such fake images to avoid abusing them. In this paper, we propose a fake image detection using an ensemble model of convolutional neural network (CNN) models that focus on deepfake detection of individual face parts. Our results show that a combination of deepfake detection based on different face parts is effective. This idea can be adopted on partially manipulated deepfake images/videos.
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