Using Wavelet Domain Fingerprints to Improve Source Camera Identification

TL;DR

This paper introduces a novel wavelet domain fingerprinting method for source camera identification that enhances detection accuracy and processing speed by avoiding the inversion step in wavelet denoising.

Contribution

The paper proposes a wavelet domain fingerprint approach that simplifies and accelerates camera source identification compared to traditional methods.

Findings

Higher detection accuracy on real-world datasets

Significant improvement in processing speed

Elimination of the inversion step in wavelet denoising

Abstract

Camera fingerprint detection plays a crucial role in source identification and image forensics, with wavelet denoising approaches proving to be particularly effective in extracting sensor pattern noise (SPN). In this article, we propose a modification to wavelet-based SPN extraction. Rather than constructing the fingerprint as an image, we introduce the notion of a wavelet domain fingerprint. This avoids the final inversion step of the denoising algorithm and allows fingerprint comparisons to be made directly in the wavelet domain. As such, our modification streamlines the extraction and comparison process. Experimental results on real-world datasets demonstrate that our method not only achieves higher detection accuracy but can also significantly improve processing speed.