# A Classification Engine for Image Ballistics of Social Data

arXiv: https://arxiv.org/abs/1610.06347 · HTML: https://mlsanity.com/paper/1610.06347/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2016.

## Built on by (most cited first, 4 of 4)

- [GenAI Mirage: The Impostor Bias and the Deepfake Detection Challenge in the Era of Artificial Illusions](https://mlsanity.com/paper/2312.16220/) (2023)
- [Deepfake Style Transfer Mixture: a First Forensic Ballistics Study on Synthetic Images](https://mlsanity.com/paper/2203.09928/) (2022)
- [An Innovative Tool for Uploading/Scraping Large Image Datasets on Social Networks](https://mlsanity.com/paper/2311.09237/) (2023)
- [Learning Double-Compression Video Fingerprints Left from Social-Media Platforms](https://mlsanity.com/paper/2212.03658/) (2022)

## Abstract (arXiv)

Image Forensics has already achieved great results for the source camera identification task on images. Standard approaches for data coming from Social Network Platforms cannot be applied due to different processes involved (e.g., scaling, compression, etc.). Over 1 billion images are shared each day on the Internet and obtaining information about their history from the moment they were acquired could be exploited for investigation purposes. In this paper, a classification engine for the reconstruction of the history of an image, is presented. Specifically, exploiting K-NN and decision trees classifiers and a-priori knowledge acquired through image analysis, we propose an automatic approach that can understand which Social Network Platform has processed an image and the software application used to perform the image upload. The engine makes use of proper alterations introduced by each platform as features. Results, in terms of global accuracy on a dataset of 2720 images, confirm the effectiveness of the proposed strategy.