# PRNU-Based Source Device Attribution for YouTube Videos

## TL;DR

This paper introduces a new PRNU-based method for source device attribution of YouTube videos that accounts for video compression effects, significantly improving accuracy over existing frame-based approaches.

## Contribution

It proposes a novel PRNU fingerprint estimation technique that considers compression effects, enhancing source attribution accuracy for compressed videos.

## Key findings

- Method outperforms existing frame-based techniques on YouTube videos.
- Effective on both I frames and all frames, especially in compressed videos.
- Demonstrates robustness against video re-compression and low bit-rate scenarios.

## Abstract

Photo Response Non-Uniformity (PRNU) is a camera imaging sensor imperfection which has earned a great interest for source device attribution of digital videos. A majority of recent researches about PRNU-based source device attribution for digital videos do not take into consideration the effects of video compression on the PRNU noise in video frames, but rather consider video frames as isolated images of equal importance. As a result, these methods perform poorly on re-compressed or low bit-rate videos. This paper proposes a novel method for PRNU fingerprint estimation from video frames taking into account the effects of video compression on the PRNU noise in these frames. With this method, we aim to determine whether two videos from unknown sources originate from the same device or not. Experimental results on a large set of videos show that the method we propose is more effective than existing frame-based methods that use either only I frames or all (I-B-P) frames, especially on YouTube videos.