Demo Data from EDBT 2026 / reviewers in the wild / expert
Pablo Pérez-Miguélez
dblp:407/8209
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Digital forensics and information hiding › digital forensics › multimedia forensics
image forensics |
1.0 | 1 | 2026 | 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 |
0.3 | 1 | 2026 | Apple's Synthetic Defocus Noise Pattern: Characterization and Forensic Applications · IEEE Trans. Inf. Forensics Secur. 2026 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Apple's Synthetic Defocus Noise Pattern: Characterization and Forensic ApplicationsabstractiPhone 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 |