苹果手机虚化区域有独特噪声模式，可用来识别设备型号和系统版本。
Apple's Synthetic Defocus Noise Pattern: Characterization and Forensic Applications
- 发现并建模iPhone虚化区域的特有噪声模式SDNP。
- 该模式受亮度、ISO等影响，可跨机型跨系统追踪照片来源。
- 掩蔽该区域能显著降低图像源识别的误报率，提升取证准确度。
iPhone人像模式图像在景深模糊区域存在一种独特的噪声模式，称为苹果合成虚化噪声模式（SDNP）。若忽略此模式，可能干扰盲式法证分析，尤其是基于PRNU的相机源验证。由于此前对SDNP研究不足，本文首次详细表征其特性，提出精确估计方法，并建模其与场景亮度、ISO设置等因素的依赖关系。基于此，我们探索了其法证应用：在开放集场景下实现跨型号、跨iOS版本的人像图像溯源；评估其在后处理下的鲁棒性。此外，实验表明，在基于PRNU的相机源验证中掩蔽受影响区域，可显著降低误报率，克服现有技术关键缺陷，优于当前最先进方法。
原文摘要 · Abstract (English)
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.
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