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Pablo Pérez-Miguélez

dblp:407/8209 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2026
0009-0001-2847-0691ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Security and privacy · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Network and information security
1 paper
Digital forensics and information hiding · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Digital forensics and information hiding › digital forensics › multimedia forensics
image forensics
1.012026
Apple's Synthetic Defocus Noise Pattern: Characterization and Forensic Applications · IEEE Trans. Inf. Forensics Secur. 2026
Digital forensics and information hiding › acquisition device identification
PRNU analysis
0.312026
Apple's Synthetic Defocus Noise Pattern: Characterization and Forensic Applications · IEEE Trans. Inf. Forensics Secur. 2026
YearPublicationVenuePosition
2026 Apple's Synthetic Defocus Noise Pattern: Characterization and Forensic Applications
abstract
iPhone portrait-mode images contain a distinctive pattern in out-of-focus regions simulating the bokeh effect, which we term Apple’sSynthetic 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.
David Vazquez-Padin, Fernando Pérez-González, Pablo Pérez-Miguélez
IEEE Trans. Inf. Forensics Secur.3