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arXiv · 2507.01712

Using Wavelet Domain Fingerprints to Improve Source Camera Identification

Xinle Tian·Matthew Nunes·Emiko Dupont·Shaunagh Downing·Freddie Lichtenstein·Matt Burns
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Abstract

Camera fingerprint detection plays a crucial role in source identification and image forensics, with wavelet denoising approaches proving particularly effective for extracting sensor pattern noise (SPN). In this article, we introduce the concept of a wavelet domain (WD) fingerprint, redefining the representation of the extracted fingerprint from the conventional image domain to the native wavelet coefficient domain. Rather than reconstructing the fingerprint as a spatial domain image, fingerprint comparison is performed directly on the wavelet coefficients, eliminating the final inverse transform and subsequent image-domain post-processing. This reformulation streamlines the fingerprint extraction and comparison pipeline while preserving the information required for source camera identification. The proposed framework is applicable to existing wavelet-based SPN extraction methods and is demonstrated using two representative state-of-the-art pipelines. Experimental results on real-world datasets show that the proposed approach significantly reduces computational cost, making it well-suited for large-scale source camera identification applications.

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Xinle Tian, Matthew Nunes, Emiko Dupont, Shaunagh Downing, Freddie Lichtenstein, Matt Burns. 2025-07-02. Using Wavelet Domain Fingerprints to Improve Source Camera Identification. https://doi.org/10.1016/j.fsidi.2026.302168

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