MTN: Forensic Analysis of MP4 Video Files Using Graph Neural Networks
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.
Cite
Text
Xiang et al. "MTN: Forensic Analysis of MP4 Video Files Using Graph Neural Networks." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2023. doi:10.1109/CVPRW59228.2023.00103Markdown
[Xiang et al. "MTN: Forensic Analysis of MP4 Video Files Using Graph Neural Networks." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2023.](https://mlanthology.org/cvprw/2023/xiang2023cvprw-mtn/) doi:10.1109/CVPRW59228.2023.00103BibTeX
@inproceedings{xiang2023cvprw-mtn,
title = {{MTN: Forensic Analysis of MP4 Video Files Using Graph Neural Networks}},
author = {Xiang, Ziyue and Yadav, Amit Kumar Singh and Bestagini, Paolo and Tubaro, Stefano and Delp, Edward J.},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2023},
pages = {963-972},
doi = {10.1109/CVPRW59228.2023.00103},
url = {https://mlanthology.org/cvprw/2023/xiang2023cvprw-mtn/}
}