A Classification Engine for Image Ballistics of Social Data

TL;DR

This paper presents an automatic classification engine that identifies the social network platform and software used for image uploads by analyzing platform-specific alterations, aiding digital investigations.

Contribution

It introduces a novel approach using K-NN and decision trees to detect social media processing signatures in images, which was not previously addressed.

Findings

Achieved high accuracy on a dataset of 2720 images.

Effectively distinguishes platforms and software based on image alterations.

Demonstrates potential for forensic analysis of social media images.

Abstract

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.