# Learning Double-Compression Video Fingerprints Left from Social-Media Platforms

arXiv: https://arxiv.org/abs/2212.03658 · HTML: https://mlsanity.com/paper/2212.03658/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 21 references that are in the map.
- First posted on arXiv 7 Dec 2022.

## Builds on (most cited first, 3 of 3)

- [Localization of JPEG double compression through multi-domain convolutional neural networks](https://mlsanity.com/paper/1706.01788/) (2017)
- [A Classification Engine for Image Ballistics of Social Data](https://mlsanity.com/paper/1610.06347/) (2016)
- [PRNU-Based Source Device Attribution for YouTube Videos](https://mlsanity.com/paper/1903.09141/) (2019)

## Abstract (arXiv)

Social media and messaging apps have become major communication platforms. Multimedia contents promote improved user engagement and have thus become a very important communication tool. However, fake news and manipulated content can easily go viral, so, being able to verify the source of videos and images as well as to distinguish between native and downloaded content becomes essential. Most of the work performed so far on social media provenance has concentrated on images; in this paper, we propose a CNN architecture that analyzes video content to trace videos back to their social network of origin. The experiments demonstrate that stating platform provenance is possible for videos as well as images with very good accuracy.