Camera Source Identification Using Discrete Cosine Transform Residue Features and Ensemble Classifier
Abstract
Machine Learning based model building and classification has proved to be extremely effective for the camera source identification problem. In this paper, we have proposed a camera source identification methodology, based on extraction of the Discrete Cosine Transform Residual features, and subsequent Random Forest based ensemble classification with AdaBoost. We improve the classification accuracy by incorporating dimensionality reduction by Principal Component Analysis. Our experimental results on 10,507 images captured by ten cameras from the Dresden Image Database gives an average classification accuracy of 99.1%, and also show low overfitting trends when the constructed classifier is applied on a different image database.
Cite
Text
Roy et al. "Camera Source Identification Using Discrete Cosine Transform Residue Features and Ensemble Classifier." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2017. doi:10.1109/CVPRW.2017.231Markdown
[Roy et al. "Camera Source Identification Using Discrete Cosine Transform Residue Features and Ensemble Classifier." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2017.](https://mlanthology.org/cvprw/2017/roy2017cvprw-camera/) doi:10.1109/CVPRW.2017.231BibTeX
@inproceedings{roy2017cvprw-camera,
title = {{Camera Source Identification Using Discrete Cosine Transform Residue Features and Ensemble Classifier}},
author = {Roy, Aniket and Chakraborty, Rajat Subhra and Sameer, Venkata Udaya and Naskar, Ruchira},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2017},
pages = {1848-1854},
doi = {10.1109/CVPRW.2017.231},
url = {https://mlanthology.org/cvprw/2017/roy2017cvprw-camera/}
}