Classification of Deepfake Videos Using Pre-trained Convolutional Neural Networks

Momina Masood, Marriam Nawaz, A. Javed, Tahira Nazir, Awais Mehmood, Rabbia Mahum
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引用次数: 7

Abstract

The advancement of Artificial Intelligence (AI) has brought a revolution in the field of information technology. Furthermore, AI has empowered the new applications to run with minimum resources and computational cost. One of such applications is Deepfakes, which produces extensively altered and modified multimedia content. However, such manipulated visual data imposed a severe threat to the security and privacy of people and can cause massive sect, religious, political, and communal stress around the globe. Now, the face-swapped base visual content is difficult to recognizable by humans through their naked eyes due to the advancement of Generative adversarial networks (GANs). Therefore, identifying such forgeries is a challenging task for the research community. In this paper, we have introduced a pipeline for identifying and detecting person faces from input visual samples. In the second step, several deep learning (DL) based approaches are employed to compute the deep features from extracted faces. Lastly, a classifier namely SVM is trained over these features to classify the data as real or manipulated. We have performed the performance comparison of various feature extractors and confirmed from reported results that DenseNet-169 along with SVM classifier outperforms the rest of the methods.
使用预训练卷积神经网络对Deepfake视频进行分类
人工智能(AI)的发展给信息技术领域带来了一场革命。此外,人工智能使新的应用程序能够以最小的资源和计算成本运行。其中一个应用程序是Deepfakes,它可以产生大量修改和修改的多媒体内容。然而,这种被操纵的视觉数据对人们的安全和隐私构成了严重威胁,并可能在全球范围内造成大规模的教派、宗教、政治和社区压力。现在,由于生成对抗网络(GANs)的进步,人脸交换的基础视觉内容很难被人类通过肉眼识别。因此,鉴别这类伪造品对研究界来说是一项具有挑战性的任务。本文介绍了一种从输入的视觉样本中识别和检测人脸的流水线。第二步,采用几种基于深度学习(DL)的方法从提取的人脸中计算深度特征。最后,在这些特征上训练一个分类器,即支持向量机,将数据分类为真实的或被操纵的。我们对各种特征提取器进行了性能比较,并从报告的结果中证实,DenseNet-169与SVM分类器一起优于其他方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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