English

Apple's Synthetic Defocus Noise Pattern: Characterization and Forensic Applications

Computer Vision and Pattern Recognition 2026-03-05 v2 Cryptography and Security Image and Video Processing

Abstract

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.

Keywords

Cite

@article{arxiv.2505.07380,
  title  = {Apple's Synthetic Defocus Noise Pattern: Characterization and Forensic Applications},
  author = {David Vázquez-Padín and Fernando Pérez-González and Pablo Pérez-Miguélez},
  journal= {arXiv preprint arXiv:2505.07380},
  year   = {2026}
}

Comments

The last version of the paper is now published in IEEE Transactions on Information Forensics & Security, vol. 21, pp. 1096-1111, 2026

R2 v1 2026-06-28T23:29:17.872Z