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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.01712 |
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| _version_ | 1866913922915762176 |
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| author | Tian, Xinle Nunes, Matthew Dupont, Emiko Downing, Shaunagh Lichtenstein, Freddie Burns, Matt |
| author_facet | Tian, Xinle Nunes, Matthew Dupont, Emiko Downing, Shaunagh Lichtenstein, Freddie Burns, Matt |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_01712 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Using Wavelet Domain Fingerprints to Improve Source Camera Identification Tian, Xinle Nunes, Matthew Dupont, Emiko Downing, Shaunagh Lichtenstein, Freddie Burns, Matt Computer Vision and Pattern Recognition Image and Video Processing Applications 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. |
| title | Using Wavelet Domain Fingerprints to Improve Source Camera Identification |
| topic | Computer Vision and Pattern Recognition Image and Video Processing Applications |
| url | https://arxiv.org/abs/2507.01712 |