Abstract
Camera source identification is one of the fundamental problems in digital image forensics. In this chapter, at the outset, the reader is made familiar with the basic components of a modern digital camera, along with the processing, acquisition, and storage of digital images in camera sources. Then a detailed literature review has been done for efficient, blind camera source identification. One of the major and effective solutions to the problem is to extract appropriate features from the images, then train a classifier, and finally classify the test samples using that trained classifier. In this chapter, we have discussed a camera source identification methodology, based on extraction of the discrete cosine transform residual features, and subsequent random forest-based ensemble classification with AdaBoost. The classification accuracy was further improved by incorporating dimensionality reduction by principal component analysis. The experiments were performed on the Dresden Image Database, and the state-of-the-art techniques were compared in detail. Moreover, the proposed technique shows low overfitting trends when the constructed classifier for one image database is applied to a different image database.
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Roy, A., Dixit, R., Naskar, R., Chakraborty, R.S. (2020). Camera Source Identification . In: Digital Image Forensics. Studies in Computational Intelligence, vol 755. Springer, Singapore. https://doi.org/10.1007/978-981-10-7644-2_2
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DOI: https://doi.org/10.1007/978-981-10-7644-2_2
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