An Efficient Deep Video Model For Deepfake Detection

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An Efficient Deep Video Model For Deepfake Detection
Title:
An Efficient Deep Video Model For Deepfake Detection
Journal Title:
2023 IEEE International Conference on Image Processing (ICIP)
Keywords:
Publication Date:
11 September 2023
Citation:
Sun, R., Zhao, Z., Shen, L., Zeng, Z., Li, Y., Veeravalli, B., & Xulei, Y. (2023). An Efficient Deep Video Model For Deepfake Detection. 2023 IEEE International Conference on Image Processing (ICIP). https://doi.org/10.1109/icip49359.2023.10222682
Abstract:
The use of deep learning technology to manipulate images and videos of people in ways that are difficult to distinguish from the real ones, known as deepfake, has become a matter of national security concern in recent years. As a result, many studies have been carried out to detect deepfake and manipulated media. Among these studies, deep video models based on convolutional neural networks have been the preferred method for detecting deepfake in videos. This study presents a novel deep video model called Sequential-Parallel Networks (SPNet) that provides efficient deepfake detection. The SPNet model consists of a simple yet innovative sequential-parallel block that first extracts spatial and temporal features sequentially, then concatenates them together in parallel. As a result, the presented SPNet possesses comparable spatio-temporal modeling abilities as most state-of-the-art deep video methods but with lower computation complexity and fewer parameters. The efficiency of the presented SPNet is demonstrated on a large-scale deepfake benchmark in terms of high recognition accuracy and low computational cost.
License type:
Publisher Copyright
Funding Info:
There was no specific funding for the research done
Description:
© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
978-1-7281-9835-4
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