Learning Double-Compression Video Fingerprints Left from Social-Media Platforms

TL;DR

This paper introduces a CNN-based method for identifying the social media platform origin of videos, enhancing content verification and authenticity detection on social media.

Contribution

It presents a novel CNN architecture specifically designed for video provenance analysis, extending prior image-focused approaches to videos.

Findings

High accuracy in platform provenance detection for videos

Effective differentiation between native and downloaded content

Applicable to various social media platforms

Abstract

Social media and messaging apps have become major communication platforms. Multimedia contents promote improved user engagement and have thus become a very important communication tool. However, fake news and manipulated content can easily go viral, so, being able to verify the source of videos and images as well as to distinguish between native and downloaded content becomes essential. Most of the work performed so far on social media provenance has concentrated on images; in this paper, we propose a CNN architecture that analyzes video content to trace videos back to their social network of origin. The experiments demonstrate that stating platform provenance is possible for videos as well as images with very good accuracy.