数字媒体取证中的深度造假检测

Vurimi Veera Venkata Naga Sai Vamsi , Sukanya S. Shet , Sodum Sai Mohan Reddy , Sharon S. Rose , Sona R. Shetty , S. Sathvika , Supriya M. S. , Sahana P. Shankar
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引用次数: 10

摘要

随着科技的发展和虚假内容的容易产生,近年来媒体的操纵被大规模地进行。人工智能篡改视频或Deepfake媒体的兴起对媒体诚信构成了巨大威胁,并正在社交媒体平台上广泛制作和传播,对其进行检测被视为一项重大挑战。本文提出了一种用于深度造假检测的方法。ResNext使用卷积神经网络(CNN)算法和长短期记忆(LSTM)作为检测Deepfake视频的方法。本文讨论了该方法及其步骤。在Celeb-Df数据集上开发的深度学习(DL)模型获得的精度为91%。
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Deepfake detection in digital media forensics

With the development of technology and ease of creation of fake content, the manipulation of media is carried out on a large scale in recent times. The rise of AI altered videos or Deepfake media has posed a great threat to media integrity and is being produced and spread widely across social media platforms, the detection of which is seen to be a major challenge. In this paper, an approach for Deepfake detection has been provided. ResNext, a Convolutional Neural Network (CNN) algorithm and Long Short-Term Memory (LSTM) is used as an approach to detect the Deepfake videos. The approach and its steps are discussed in this paper. The accuracy obtained for the developed Deep-Learning (DL) model over the Celeb-Df dataset is 91%.

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