A novel approach for detecting deep fake videos using graph neural network

IF 8.6 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS Journal of Big Data Pub Date : 2024-02-01 DOI:10.1186/s40537-024-00884-y

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Abstract

Deep fake technology has emerged as a double-edged sword in the digital world. While it holds potential for legitimate uses, it can also be exploited to manipulate video content, causing severe social and security concerns. The research gap lies in the fact that traditional deep fake detection methods, such as visual quality analysis or inconsistency detection, need help to keep up with the rapidly advancing technology used to create deep fakes. That means there's a need for more sophisticated detection techniques. This paper introduces an enhanced approach for detecting deep fake videos using graph neural network (GNN). The proposed method splits the detection process into two phases: a mini-batch graph convolution network stream four-block CNN stream comprising Convolution, Batch Normalization, and Activation function. The final step is a flattening operation, which is essential for connecting the convolutional layers to the dense layer. The fusion of these two phases is performed using three different fusion networks: FuNet-A (additive fusion), FuNet-M (element-wise multiplicative fusion), and FuNet-C (concatenation fusion). The paper further evaluates the proposed model on different datasets, where it achieved an impressive training and validation accuracy of 99.3% after 30 epochs.

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利用图神经网络检测深度伪造视频的新方法

摘要深度伪造技术已成为数字世界的一把双刃剑。虽然它具有合法使用的潜力，但也可能被用来操纵视频内容，引发严重的社会和安全问题。研究空白在于，传统的深度伪造检测方法，如视觉质量分析或不一致性检测，需要帮助才能跟上用于制造深度伪造的快速发展的技术。这意味着需要更复杂的检测技术。本文介绍了一种利用图神经网络（GNN）检测深度伪造视频的增强型方法。所提出的方法将检测过程分为两个阶段：迷你批量图卷积网络流四块 CNN 流，包括卷积、批量归一化和激活函数。最后一步是扁平化操作，这对于连接卷积层和稠密层至关重要。这两个阶段的融合使用了三种不同的融合网络：FuNet-A（加法融合）、FuNet-M（元素乘法融合）和 FuNet-C（串联融合）。论文进一步在不同的数据集上对所提出的模型进行了评估，在 30 个历时之后，该模型的训练和验证准确率达到了令人印象深刻的 99.3%。

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来源期刊

Journal of Big Data Computer Science-Information Systems

CiteScore

17.80

自引率

3.70%

发文量

105

审稿时长

13 weeks

期刊介绍： The Journal of Big Data publishes high-quality, scholarly research papers, methodologies, and case studies covering a broad spectrum of topics, from big data analytics to data-intensive computing and all applications of big data research. It addresses challenges facing big data today and in the future, including data capture and storage, search, sharing, analytics, technologies, visualization, architectures, data mining, machine learning, cloud computing, distributed systems, and scalable storage. The journal serves as a seminal source of innovative material for academic researchers and practitioners alike.