# We Need No Pixels: Video Manipulation Detection Using Stream Descriptors

arXiv: https://arxiv.org/abs/1906.08743 · HTML: https://mlsanity.com/paper/1906.08743/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2019.

## Built on by (most cited first, 7 of 7)

- [Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forward](https://mlsanity.com/paper/2103.00484/) (2021)
- [An Overview of Recent Work in Media Forensics: Methods and Threats](https://mlsanity.com/paper/2204.12067/) (2022)
- [Efficient video integrity analysis through container characterization](https://mlsanity.com/paper/2101.10795/) (2021)
- [Synthesized Speech Detection Using Convolutional Transformer-Based Spectrogram Analysis](https://mlsanity.com/paper/2205.01800/) (2022)
- [Frequency Domain-Based Detection of Generated Audio](https://mlsanity.com/paper/2205.01806/) (2022)
- [Forensic Analysis of Video Files Using Metadata](https://mlsanity.com/paper/2105.06361/) (2021)
- [Deep-Fake CAPTCHA: Mitigating Next-Generation Social Engineering Attacks](https://mlsanity.com/paper/2609.11404/) (2026)

## Abstract (arXiv)

Manipulating video content is easier than ever. Due to the misuse potential of manipulated content, multiple detection techniques that analyze the pixel data from the videos have been proposed. However, clever manipulators should also carefully forge the metadata and auxiliary header information, which is harder to do for videos than images. In this paper, we propose to identify forged videos by analyzing their multimedia stream descriptors with simple binary classifiers, completely avoiding the pixel space. Using well-known datasets, our results show that this scalable approach can achieve a high manipulation detection score if the manipulators have not done a careful data sanitization of the multimedia stream descriptors.