A Classification Engine for Image Ballistics of Social Data
This work addresses a domain-specific problem for forensic investigators by enabling automatic identification of the social network platform and upload software used for images, though it is incremental as it builds on existing image forensics methods.
The paper tackles the problem of reconstructing the history of images shared on social networks, which is challenging due to platform-specific alterations like scaling and compression, and presents a classification engine that achieves effective results with global accuracy on a dataset of 2720 images.
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.