# Forensic Analysis of Video Files Using Metadata

arXiv: https://arxiv.org/abs/2105.06361 · HTML: https://mlsanity.com/paper/2105.06361/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 36 references that are in the map.
- First posted on arXiv 13 May 2021.

## Builds on (most cited first, 5 of 5)

- [The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation](https://mlsanity.com/paper/1802.07228/) (2018)
- [Multi-task Learning For Detecting and Segmenting Manipulated Facial Images and Videos](https://mlsanity.com/paper/1906.06876/) (2019)
- [Recurrent Convolutional Strategies for Face Manipulation Detection in Videos](https://mlsanity.com/paper/1905.00582/) (2019)
- [We Need No Pixels: Video Manipulation Detection Using Stream Descriptors](https://mlsanity.com/paper/1906.08743/) (2019)
- [Efficient video integrity analysis through container characterization](https://mlsanity.com/paper/2101.10795/) (2021)

## Built on by (most cited first, 2 of 2)

- [An Overview of Recent Work in Media Forensics: Methods and Threats](https://mlsanity.com/paper/2204.12067/) (2022)
- [H4VDM: H.264 Video Device Matching](https://mlsanity.com/paper/2210.11549/) (2022)

## Abstract (arXiv)

The unprecedented ease and ability to manipulate video content has led to a rapid spread of manipulated media. The availability of video editing tools greatly increased in recent years, allowing one to easily generate photo-realistic alterations. Such manipulations can leave traces in the metadata embedded in video files. This metadata information can be used to determine video manipulations, brand of video recording device, the type of video editing tool, and other important evidence. In this paper, we focus on the metadata contained in the popular MP4 video wrapper/container. We describe our method for metadata extractor that uses the MP4's tree structure. Our approach for analyzing the video metadata produces a more compact representation. We will describe how we construct features from the metadata and then use dimensionality reduction and nearest neighbor classification for forensic analysis of a video file. Our approach allows one to visually inspect the distribution of metadata features and make decisions. The experimental results confirm that the performance of our approach surpasses other methods.