Learning Double-Compression Video Fingerprints Left from Social-Media Platforms
This addresses the issue of fake news and manipulated content for social media users, but it is incremental as it extends existing image-based provenance methods to videos.
The paper tackled the problem of verifying the source of videos on social media platforms by proposing a CNN architecture to trace videos back to their origin, achieving very good accuracy in experiments.
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.