@InProceedings{7760435,
  author = {A. Tuama and F. Comby and M. Chaumont},
  booktitle = {2016 24th European Signal Processing Conference (EUSIPCO)},
  title = {Camera model identification based machine learning approach with high order statistics features},
  year = {2016},
  pages = {1183-1187},
  abstract = {Source camera identification methods aim at identifying the camera used to capture an image. In this paper we developed a method for digital camera model identification by extracting three sets of features in a machine learning scheme. These features are the co-occurrences matrix, some features related to CFA interpolation arrangement, and conditional probability statistics. These features give high order statistics which supplement and enhance the identification rate. The method is implemented with 14 camera models from Dresden database with multi class SVM classifier. A comparison is performed between our method and a camera fingerprint correlation-based method which only depends on PRNU extraction. The experiments prove the strength of our proposition since it achieves higher accuracy than the correlation-based method.},
  keywords = {cameras;feature extraction;higher order statistics;image capture;learning (artificial intelligence);optical filters;support vector machines;machine learning;high order statistics features;source camera identification methods;image capture;digital camera model identification;feature extraction;color filter array;conditional probability statistics;Dresden database;multiclass SVM classifier;Cameras;Feature extraction;Image color analysis;Correlation;Colored noise;Interpolation;Discrete cosine transforms;Camera identification;Co-occurrences;CFA interpolation;Conditional Probability;SVM},
  doi = {10.1109/EUSIPCO.2016.7760435},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2016/papers/1570256116.pdf},
}

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These features are the co-occurrences matrix, some features related to CFA interpolation arrangement, and conditional probability statistics. These features give high order statistics which supplement and enhance the identification rate. The method is implemented with 14 camera models from Dresden database with multi class SVM classifier. A comparison is performed between our method and a camera fingerprint correlation-based method which only depends on PRNU extraction. 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Chaumont},\n booktitle = {2016 24th European Signal Processing Conference (EUSIPCO)},\n title = {Camera model identification based machine learning approach with high order statistics features},\n year = {2016},\n pages = {1183-1187},\n abstract = {Source camera identification methods aim at identifying the camera used to capture an image. In this paper we developed a method for digital camera model identification by extracting three sets of features in a machine learning scheme. These features are the co-occurrences matrix, some features related to CFA interpolation arrangement, and conditional probability statistics. These features give high order statistics which supplement and enhance the identification rate. The method is implemented with 14 camera models from Dresden database with multi class SVM classifier. A comparison is performed between our method and a camera fingerprint correlation-based method which only depends on PRNU extraction. 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