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A study of co-occurrence based local features for camera model identification

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Abstract

Camera model identification has great relevance for many forensic applications, and is receiving growing attention in the literature. Virtually all techniques rely on the traces left in the image by the long sequence of in-camera processes which are specific of each model. They differ in the prior assumptions, if any, and in how such evidence is gathered in expressive features. In this work we study a class of blind features, based on the analysis of the image residuals of all color bands. They are extracted locally, based on co-occurrence matrices of selected neighbors, and then used to train a classifier. A number of experiments are carried out on the well-known Dresden Image Database. Besides the full-knowledge case, where all models of interest are known in advance, other scenarios with more limited knowledge and partially corrupted images are also investigated. Experimental results show these features to provide a state-of-the-art performance.

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Acknowledgment

This work was partially funded by the Italian Ministry of Education, University and Research (MIUR) within the framework of the project PAC02L1_00050 AETERNUUM.

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Authors and Affiliations

  1. University Federico II of Naples, Naples, Italy

    Francesco Marra, Giovanni Poggi, Carlo Sansone & Luisa Verdoliva

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  1. Francesco Marra
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  2. Giovanni Poggi
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  3. Carlo Sansone
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  4. Luisa Verdoliva
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Correspondence to Giovanni Poggi.

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Marra, F., Poggi, G., Sansone, C. et al. A study of co-occurrence based local features for camera model identification. Multimed Tools Appl 76, 4765–4781 (2017). https://doi.org/10.1007/s11042-016-3663-0

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  • DOI: https://doi.org/10.1007/s11042-016-3663-0

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