Camera Fingerprint Extraction via Spatial Domain Averaged Frames

TL;DR

This paper introduces a fast and efficient method for camera fingerprint extraction using Spatial Domain Averaged frames, significantly reducing processing time with minimal impact on accuracy.

Contribution

The paper proposes a novel technique utilizing SDA-frames to accelerate camera fingerprint extraction, achieving over 50 times faster processing with comparable results.

Findings

Method reduces denoising operations by over 50 times.

Fingerprint accuracy remains high despite faster processing.

Applicable to large-scale image and video datasets.

Abstract

Photo Response Non-Uniformity (PRNU) based camera attribution is an effective method to determine the source camera of visual media (an image or a video). To apply this method, images or videos need to be obtained from a camera to create a "camera fingerprint" which then can be compared against the PRNU of the query media whose origin is under question. The fingerprint extraction process can be time-consuming when a large number of video frames or images have to be denoised. This may need to be done when the individual images have been subjected to high compression or other geometric processing such as video stabilization. This paper investigates a simple, yet effective and efficient technique to create a camera fingerprint when so many still images need to be denoised. The technique utilizes Spatial Domain Averaged (SDA) frames. An SDA-frame is the arithmetic mean of multiple still images. When it is used for fingerprint extraction, the number of denoising operations can be significantly decreased with little or no performance loss. Experimental results show that the proposed method can work more than 50 times faster than conventional methods while providing similar matching results.