Apple's Synthetic Defocus Noise Pattern: Characterization and Forensic Applications
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866917313226211328 |
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| author | Vázquez-Padín, David Pérez-González, Fernando Pérez-Miguélez, Pablo |
| author_facet | Vázquez-Padín, David Pérez-González, Fernando Pérez-Miguélez, Pablo |
| contents | iPhone portrait-mode images contain a distinctive pattern in out-of-focus regions simulating the bokeh effect, which we term Apple's Synthetic Defocus Noise Pattern (SDNP). If overlooked, this pattern can interfere with blind forensic analyses, especially PRNU-based camera source verification, as noted in earlier works. Since Apple's SDNP remains underexplored, we provide a detailed characterization, proposing a method for its precise estimation, modeling its dependence on scene brightness, ISO settings, and other factors. Leveraging this characterization, we explore forensic applications of the SDNP, including traceability of portrait-mode images across iPhone models and iOS versions in open-set scenarios, assessing its robustness under post-processing. Furthermore, we show that masking SDNP-affected regions in PRNU-based camera source verification significantly reduces false positives, overcoming a critical limitation in camera attribution, and improving state-of-the-art techniques. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_07380 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Apple's Synthetic Defocus Noise Pattern: Characterization and Forensic Applications Vázquez-Padín, David Pérez-González, Fernando Pérez-Miguélez, Pablo Computer Vision and Pattern Recognition Cryptography and Security Image and Video Processing iPhone portrait-mode images contain a distinctive pattern in out-of-focus regions simulating the bokeh effect, which we term Apple's Synthetic Defocus Noise Pattern (SDNP). If overlooked, this pattern can interfere with blind forensic analyses, especially PRNU-based camera source verification, as noted in earlier works. Since Apple's SDNP remains underexplored, we provide a detailed characterization, proposing a method for its precise estimation, modeling its dependence on scene brightness, ISO settings, and other factors. Leveraging this characterization, we explore forensic applications of the SDNP, including traceability of portrait-mode images across iPhone models and iOS versions in open-set scenarios, assessing its robustness under post-processing. Furthermore, we show that masking SDNP-affected regions in PRNU-based camera source verification significantly reduces false positives, overcoming a critical limitation in camera attribution, and improving state-of-the-art techniques. |
| title | Apple's Synthetic Defocus Noise Pattern: Characterization and Forensic Applications |
| topic | Computer Vision and Pattern Recognition Cryptography and Security Image and Video Processing |
| url | https://arxiv.org/abs/2505.07380 |