# Deep learning for source camera identification on mobile devices

arXiv: https://arxiv.org/abs/1710.01257 · HTML: https://mlsanity.com/paper/1710.01257/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2017.

## Built on by (most cited first, 9 of 9)

- [One-shot Representational Learning for Joint Biometric and Device Authentication](https://mlsanity.com/paper/2101.00524/) (2021)
- [Beyond PRNU: Learning Robust Device-Specific Fingerprint for Source Camera Identification](https://mlsanity.com/paper/2111.02144/) (2021)
- [Medicinal Boxes Recognition on a Deep Transfer Learning Augmented Reality Mobile Application](https://mlsanity.com/paper/2203.14031/) (2022)
- [Hidden Classification Layers: Enhancing linear separability between classes in neural networks layers](https://mlsanity.com/paper/2306.06146/) (2023)
- [Automated Customization of On-Thing Inference for Quality-of-Experience Enhancement](https://mlsanity.com/paper/2112.06918/) (2021)
- [Digital Image Forensics using Deep Learning](https://mlsanity.com/paper/2210.09052/) (2022)
- [Towards Secure AI-driven Industrial Metaverse with NFT Digital Twins](https://mlsanity.com/paper/2412.15716/) (2024)
- [PDC-ViT : Source Camera Identification using Pixel Difference Convolution and Vision Transformer](https://mlsanity.com/paper/2501.16227/) (2025)
- [Using Wavelet Domain Fingerprints to Improve Source Camera Identification](https://mlsanity.com/paper/2507.01712/) (2025)

## Abstract (arXiv)

In the present paper, we propose a source camera identification method for mobile devices based on deep learning. Recently, convolutional neural networks (CNNs) have shown a remarkable performance on several tasks such as image recognition, video analysis or natural language processing. A CNN consists on a set of layers where each layer is composed by a set of high pass filters which are applied all over the input image. This convolution process provides the unique ability to extract features automatically from data and to learn from those features. Our proposal describes a CNN architecture which is able to infer the noise pattern of mobile camera sensors (also known as camera fingerprint) with the aim at detecting and identifying not only the mobile device used to capture an image (with a 98% of accuracy), but also from which embedded camera the image was captured. More specifically, we provide an extensive analysis on the proposed architecture considering different configurations. The experiment has been carried out using the images captured from different mobile devices cameras (MICHE-I Dataset was used) and the obtained results have proved the robustness of the proposed method.