用深度学习和Gabor过滤器检测深度伪造

IF 1.2 Q3 MULTIDISCIPLINARY SCIENCES
Wildan J. Jameel, S. Kadhem, A. Abbas
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引用次数: 1

摘要

许多基于人工智能技术的编辑程序的激增促成了深度造假技术的出现。Deepfakes致力于通过让一个人做他从未做过或说过的话来制造和伪造事实。因此,开发一种深度造假检测算法对于区分真假媒体是非常重要的。卷积神经网络(cnn)是最复杂的分类器之一,但选择提供给这些网络的数据的性质非常重要。出于这个原因,我们使用16个Gabor滤波器捕获输入数据帧的精细纹理细节,然后将它们提供给二进制CNN分类器,而不是使用红-绿-蓝颜色信息。本文的目的是让读者更深入地了解(1)通过开发基于深度学习和Gabor滤波器的新模型来提高区分假面部图像和真实面部图像的效率,以及(2)如果将深度学习(CNN)与取证工具(Gabor滤波器)相结合,如何有助于检测深度伪造。实验表明,训练准确率达到98.06%左右,验证率达到97.50%。与目前最先进的方法相比,该模型具有更高的效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detecting Deepfakes with Deep Learning and Gabor Filters
The proliferation of many editing programs based on artificial intelligence techniques has contributed to the emergence of deepfake technology. Deepfakes are committed to fabricating and falsifying facts by making a person do actions or say words that he never did or said. So that developing an algorithm for deepfakes detection is very important to discriminate real from fake media. Convolutional neural networks (CNNs) are among the most complex classifiers, but choosing the nature of the data fed to these networks is extremely important. For this reason, we capture fine texture details of input data frames using 16 Gabor filters indifferent directions and then feed them to a binary CNN classifier instead of using the red-green-blue color information. The purpose of this paper is to give the reader a deeper view of (1) enhancing the efficiency of distinguishing fake facial images from real facial images by developing a novel model based on deep learning and Gabor filters and (2) how deep learning (CNN) if combined with forensic tools (Gabor filters) contributed to the detection of deepfakes. Our experiment shows that the training accuracy reaches about 98.06% and 97.50% validation. Likened to the state-of-the-art methods, the proposed model has higher efficiency.
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来源期刊
ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY
ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY MULTIDISCIPLINARY SCIENCES-
自引率
33.30%
发文量
33
审稿时长
16 weeks
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