Camera Model Identification using Deep CNN and Transfer Learning Approach. Banna, M. H. A., Haider, M. A., Nahian, M. J. A., Islam, M. M., Taher, K. A., & Kaiser, M. S. In 2019 International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST), pages 626–630, January, 2019. doi abstract bibtex The forensic investigation on digital images is to assess the authenticity of images without the embedded security on the images. The camera model identification is the first step for image forensic investigation. The paper proposes the deep Convolutional Neural Network and transfer learning approach for extracting features from an images dataset. An open image dataset of 3900 images have been created using three camera models. Three state-of-the-art machine learning algorithms such as SVM, logistic regression and random forest based classifiers have been used for evaluating identification accuracy.
@inproceedings{banna_camera_2019,
title = {Camera {Model} {Identification} using {Deep} {CNN} and {Transfer} {Learning} {Approach}},
doi = {10.1109/ICREST.2019.8644194},
abstract = {The forensic investigation on digital images is to assess the authenticity of images without the embedded security on the images. The camera model identification is the first step for image forensic investigation. The paper proposes the deep Convolutional Neural Network and transfer learning approach for extracting features from an images dataset. An open image dataset of 3900 images have been created using three camera models. Three state-of-the-art machine learning algorithms such as SVM, logistic regression and random forest based classifiers have been used for evaluating identification accuracy.},
booktitle = {2019 {International} {Conference} on {Robotics},{Electrical} and {Signal} {Processing} {Techniques} ({ICREST})},
author = {Banna, M. H. Al and Haider, M. Ali and Nahian, M. J. Al and Islam, M. M. and Taher, K. A. and Kaiser, M. S.},
month = jan,
year = {2019},
keywords = {Cameras, Classification, Convolution, Deep CNN, Deep learning, Feature extraction, Machine Learning, MobileNet, Robot vision systems, SVM, Support vector machines, camera model identification, cameras, convolutional neural nets, convolutional neural network, deep CNN, digital forensics, digital images, embedded security, feature extraction, image classification, image forensic, image forensic investigation, learning (artificial intelligence), logistic regression, machine learning algorithm, open image dataset, random forest, transfer learning approach},
pages = {626--630},
}
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