2025 Publications and Research Highlights
As part of my ongoing work in independent forensic method research, 2025 included several publications in the Journal of Forensic Sciences that advanced foundational understanding in PDF image structures, iOS AAC encoding behavior, and cloud‑to‑mobile image integrity. These studies support my broader goal of strengthening the scientific reliability of digital and multimedia forensic methods through transparent, empirical, and reproducible research.
Portable Document Format (PDF) Image Embedding and Analysis
Journal of Forensic Sciences
First published: 17 November 2025
https://doi.org/10.1111/1556-4029.70229
This technical note provides a foundational introduction to the internal structures that govern how images are embedded within PDF files. The study examined object models, syntax, and embedded image behaviors using hex‑level inspection and JSON‑based structure reports aligned with ISO PDF standards.
The work identified a modular taxonomy of embedded image types and documented software‑specific behaviors in Adobe Acrobat and LibreOffice Draw, including palette‑based GIF embeddings and metadata‑retention differences.
My research perspective:
This was the initial exploratory study in my broader PDF‑embedding research program. It serves as a structural primer for examiners seeking to understand how embedded images are represented internally and how those structures can be interpreted and validated during forensic analysis.
Quantitative Study of Zero‑Amplitude Sample Padding in iOS AAC Encoding
Journal of Forensic Sciences
First published: 19 August 2025
https://doi.org/10.1111/1556-4029.70157
This study examined the behavior of zero‑amplitude sample padding (“zero‑padding”) in AAC audio recordings generated by iOS devices. Using 100 recordings across 11 devices, the research measured pre‑ and post‑signal padding under controlled noise conditions and compared results across multiple analysis tools.
The findings revealed significant variability in pre‑signal padding, far exceeding Apple’s documented priming values, and demonstrated that background noise measurably influences padding behavior. Tool‑dependent differences were also observed in post‑signal padding.
My research perspective:
This work is part of a multi‑phase effort to map error sources relevant to audio stream hashing. Understanding zero‑padding behavior is essential for designing mitigation strategies and planning the upcoming audio stream hashing validation study. It represents one component of a larger audio stream hashing error‑analysis series.
Exploring Dropbox Image Downloads to iPhone via Safari
Journal of Forensic Sciences
First published: 01 September 2025
https://doi.org/10.1111/1556-4029.70173
This validation study assessed the integrity of images downloaded from Dropbox to iPhones via Safari, an evidence‑collection scenario often used when specialized tools are unavailable. The research compared downloads saved to the Files folder versus the Photos application across multiple iPhone devices and iOS versions.
Results showed that pixel‑level content remained unchanged in all cases (100% SHA‑256 stream‑hash matches), while container‑level structures were modified only within the Photos application. MS‑SSIM scores remained at 1.0, indicating no perceptual degradation.
My research perspective:
This project originated as a graduate‑level research assignment that I expanded with a small student group. It provided a controlled, quantitative look at a common acquisition workflow and helped clarify where structural changes occur during cloud‑to‑mobile transfers.
A Research‑Focused Framework for Empirical Method Validation in Digital and Multimedia Evidence
Journal of Forensic Sciences
First published: 04 January 2026
https://doi.org/10.1111/1556-4029.70253
Although published in early 2026, this paper represents my a significant 2025 research and documentation effort. It introduces a structured, research‑focused framework for empirical method validation in digital and multimedia forensic science. The framework adapts validation principles from traditional forensic disciplines and integrates guidance from NAS, PCAST, NIST, Daubert, and Federal Rule of Evidence 702.
The model outlines ten iterative steps, including dataset control, pilot calibration, error mapping, and community review, designed to support both full empirical validation and interim litigation‑focused adaptation.
My research perspective:
This framework formalizes the methodological foundation for all of my independent research. It provides the structure I use when designing validation studies, planning statistical components, and documenting reproducible workflows across digital, multimedia, and forensic ML methods.
Closing Thoughts
Each of these studies contributes to a broader effort to strengthen the scientific foundations of digital and multimedia forensic methods. My work remains fully independent; I take no clients, offer no services, sell no products, and receive no funding. The goal is simple: to advance transparent, reproducible, and empirically grounded forensic science.
More research updates will follow as ongoing projects in PDF analysis, audio stream hashing, and forensic machine learning progress through their next phases.