MTN: Forensic Analysis of MP4 Video Files Using Graph Neural Networks

Ziyue Xiang, Amit Kumar Singh Yadav, Paolo Bestagini, Stefano Tubaro, Edward J. Delp; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2023, pp. 963-972

Abstract

MP4 video files are stored using a tree data structure. These trees contain rich information that can be used for forensic analysis. In this paper, we propose MP4 Tree Network (MTN), an approach based on an end-to-end Graph Neural Networks (GNNs) that is used for forensic analysis of MP4 trees. MTN does not use any video pixel data. MTN is trained using Self-Supervised Learning (SSL), which generates semantic-preserving node embeddings for the nodes in an MP4 tree. We also propose a data augmentation technique for MP4 trees, which helps train MTN in data-scarce scenarios. MTN achieves good performance across 3 video forensics tasks on the EVA-7K dataset. We show that MTN can gain more comprehensive understanding about the MP4 trees and is more robust to potential attacks compared to existing methods.

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[bibtex]
@InProceedings{Xiang_2023_CVPR, author = {Xiang, Ziyue and Yadav, Amit Kumar Singh and Bestagini, Paolo and Tubaro, Stefano and Delp, Edward J.}, title = {MTN: Forensic Analysis of MP4 Video Files Using Graph Neural Networks}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2023}, pages = {963-972} }