Journal article
Robust camera model identification using demosaicing residual features
Multimedia tools and applications, v 80(8), pp 11365-11393
2021
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
In this paper, we propose a new framework for performing accurate and robust camera model identification by fully exploiting demosaicing information in a camera’s output images. Instead of fitting a camera’s demosaicing process into parametric models, our framework works by exposing and extracting a diverse set of intra-channel and inter-channel color value correlations originated from the demosaicing process. To expose these correlations, we first apply a number of diversified baseline demosaicing algorithms to re-demosaic the image under investigation, and gather a set of both linear and nonlinear demosaicing residuals. To further extract demosaicing correlations with respect to the color filter array (CFA) structure, co-occurrence matrices are calculated using a new set of geometric patterns. These patterns are specifically designed to extract different types of color value dependencies within the repeated lattice of the CFA pattern. We design a multi-class ensemble classifier to utilize all extracted color value correlations to perform camera model identification. A series of experiments show that our proposed framework can achieve an accuracy of 98.14
%
on a database with 68 camera models, and is highly robust to post-JPEG compression and contrast enhancement.
Metrics
Details
- Title
- Robust camera model identification using demosaicing residual features
- Creators
- Chen Chen - Drexel UniversityMatthew C. Stamm - Drexel University
- Publication Details
- Multimedia tools and applications, v 80(8), pp 11365-11393
- Publisher
- Springer US
- Grant note
- 1553610 / Directorate for Computer and Information Science and Engineering (https://doi.org/10.13039/100000083)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000605119700001
- Scopus ID
- 2-s2.0-85099000880
- Other Identifier
- 991019168256904721
UN Sustainable Development Goals (SDGs)
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Information Systems
- Computer Science, Software Engineering
- Computer Science, Theory & Methods
- Engineering, Electrical & Electronic