# Camera Model Identification Using Audio and Visual Content from Videos

arXiv: https://arxiv.org/abs/2406.17916 · HTML: https://mlsanity.com/paper/2406.17916/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 31 references that are in the map.
- First posted on arXiv 25 Jun 2024.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Multimodal Deep Learning](https://mlsanity.com/paper/2301.04856/) (2023)
- [Learn to Combine Modalities in Multimodal Deep Learning](https://mlsanity.com/paper/1805.11730/) (2018)
- [Video Camera Identification from Sensor Pattern Noise with a Constrained ConvNet](https://mlsanity.com/paper/2012.06277/) (2020)
- [A Modified Fourier-Mellin Approach for Source Device Identification on Stabilized Videos](https://mlsanity.com/paper/2005.09984/) (2020)
- [Video Source Characterization Using Encoding and Encapsulation Characteristics](https://mlsanity.com/paper/2201.02949/) (2022)

## Built on by (most cited first, 1 of 1)

- [AdaCubic: An Adaptive Cubic Regularization Optimizer for Deep Learning](https://mlsanity.com/paper/2604.09437/) (2026)

## Abstract (arXiv)

The identification of device brands and models plays a pivotal role in the realm of multimedia forensic applications. This paper presents a framework capable of identifying devices using audio, visual content, or a fusion of them. The fusion of visual and audio content occurs later by applying two fundamental fusion rules: the product and the sum. The device identification problem is tackled as a classification one by leveraging Convolutional Neural Networks. Experimental evaluation illustrates that the proposed framework exhibits promising classification performance when independently using audio or visual content. Furthermore, although the fusion results don't consistently surpass both individual modalities, they demonstrate promising potential for enhancing classification performance. Future research could refine the fusion process to improve classification performance in both modalities consistently. Finally, a statistical significance test is performed for a more in-depth study of the classification results.