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.

翻译：iPhone人像模式图像在模拟散景效果的失焦区域包含一种独特的模式，我们称之为苹果合成散焦噪声模式（SDNP）。如先前研究指出，若忽略此模式，其可能干扰盲取证分析，特别是基于PRNU的相机来源验证。鉴于苹果SDNP尚未得到充分探索，本文提供了详细表征，提出一种精确估计该模式的方法，并建模其对照明亮度、ISO设置及其他因素的依赖性。基于此表征，我们探索了SDNP的取证应用，包括开放集场景下跨iPhone机型与iOS版本的人像模式图像溯源，以及评估其在后处理下的鲁棒性。此外，我们证明在基于PRNU的相机来源验证中掩蔽受SDNP影响的区域，可显著降低误报率，从而克服相机归属判定的关键局限，并改进现有最先进技术。