A New Dataset for Source Identification of High Dynamic Range Images
Omar Al Shaya1,2, Pengpeng Yang3,4, Rongrong Ni5,6
1Department of Information Engineering, University of Florence, Via di S. Marta, 3, 50139 Florence, Italy. omar.alshaya@unifi.it.
Sensors (Basel, Switzerland)
|November 9, 2018
Summary
This study introduces a new High Dynamic Range (HDR) and Standard Dynamic Range (SDR) image database. Digital source identification is challenging for HDR images due to their complexity.
Area of Science:
- Multimedia Forensics
- Image Analysis
Background:
- Digital source identification is crucial in multimedia forensics.
- Research on High Dynamic Range (HDR) images remains less explored compared to Standard Dynamic Range (SDR) images.
Purpose of the Study:
- To present a novel database of HDR and SDR images captured under diverse conditions.
- To evaluate the performance of a reference pattern noise-based source identification algorithm on HDR and SDR images.
Main Methods:
- Creation of a new database featuring HDR and SDR images.
- Inclusion of various capturing motions, scenes, and devices in the dataset.
- Application of a pattern noise-based algorithm for source identification.
Main Results:
- The reference pattern noise-based algorithm showed difficulties in identifying the source of HDR images.
- HDR images present greater complexity and a wider dynamic range, impacting identification accuracy.
- Performance varied based on capturing conditions and devices used.
Conclusions:
- Source identification in HDR images is more challenging than in SDR images.
- Capturing conditions and devices significantly influence the accuracy of digital source identification.
- Further research is needed to address the complexities of HDR image forensics.
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