# Identification of Social-Media Platform of Videos through the Use of Shared Features

arXiv: https://arxiv.org/abs/2109.03598 · HTML: https://mlsanity.com/paper/2109.03598/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 45 references that are in the map.
- First posted on arXiv 8 Sep 2021.

## Builds on (most cited first, 13 of 13)

- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [Fighting Fake News: Image Splice Detection via Learned Self-Consistency](https://mlsanity.com/paper/1805.04096/) (2018)
- [Noiseprint: a CNN-based camera model fingerprint](https://mlsanity.com/paper/1808.08396/) (2018)
- [ForensicTransfer: Weakly-supervised Domain Adaptation for Forgery Detection](https://mlsanity.com/paper/1812.02510/) (2018)
- [Forensic Similarity for Digital Images](https://mlsanity.com/paper/1902.04684/) (2019)
- [Exposing Fake Images with Forensic Similarity Graphs](https://mlsanity.com/paper/1912.02861/) (2019)
- [Combining PRNU and noiseprint for robust and efficient device source identification](https://mlsanity.com/paper/2001.06440/) (2020)
- [Efficient video integrity analysis through container characterization](https://mlsanity.com/paper/2101.10795/) (2021)
- [PRNU-Based Source Device Attribution for YouTube Videos](https://mlsanity.com/paper/1903.09141/) (2019)
- [Camera Fingerprint Extraction via Spatial Domain Averaged Frames](https://mlsanity.com/paper/1909.04573/) (2019)
- [Application of DenseNet in Camera Model Identification and Post-processing Detection](https://mlsanity.com/paper/1809.00576/) (2018)
- [Camera Model Identification Using Convolutional Neural Networks](https://mlsanity.com/paper/1810.02981/) (2018)

## Abstract (arXiv)

Videos have become a powerful tool for spreading illegal content such as military propaganda, revenge porn, or bullying through social networks. To counter these illegal activities, it has become essential to try new methods to verify the origin of videos from these platforms. However, collecting datasets large enough to train neural networks for this task has become difficult because of the privacy regulations that have been enacted in recent years. To mitigate this limitation, in this work we propose two different solutions based on transfer learning and multitask learning to determine whether a video has been uploaded from or downloaded to a specific social platform through the use of shared features with images trained on the same task. By transferring features from the shallowest to the deepest levels of the network from the image task to videos, we measure the amount of information shared between these two tasks. Then, we introduce a model based on multitask learning, which learns from both tasks simultaneously. The promising experimental results show, in particular, the effectiveness of the multitask approach. According to our knowledge, this is the first work that addresses the problem of social media platform identification of videos through the use of shared features.