Automatic source camera identification by lens aberration and JPEG compression statistics

2006 ◽  
Author(s):  
Kai-san Choi
Author(s):  
Matthew James Sorrell

We propose that the implementation of the JPEG compression algorithm represents a manufacturer and model-series specific means of identification of the source camera of a digital photographic image. Experimental results based on a database of over 5,000 photographs from 27 camera models by 10 brands shows that the choice of JPEG quantisation table, in particular, acts as an effective discriminator between model series with a high level of differentiation. Furthermore, we demonstrate that even after recompression of an image, residual artefacts of double quantisation continue to provide limited means of source camera identification, provided that certain conditions are met. Other common techniques for source camera identification are also introduced, and their strengths and weaknesses are discussed.


2020 ◽  
Vol 130 ◽  
pp. 139-147 ◽  
Author(s):  
Debbrota Paul Chowdhury ◽  
Sambit Bakshi ◽  
Pankaj Kumar Sa ◽  
Banshidhar Majhi

2011 ◽  
Vol 3 (4) ◽  
pp. 1-15
Author(s):  
Yongjian Hu ◽  
Chang-Tsun Li ◽  
Changhui Zhou ◽  
Xufeng Lin

Statistical image features play an important role in forensic identification. Current source camera identification schemes select image features mainly based on classification accuracy and computational efficiency. For forensic investigation purposes; however, these selection criteria are not enough. Consider most real-world photos may have undergone common image processing due to various reasons, source camera classifiers must have the capability to deal with those processed photos. In this work, the authors first build a sample camera classifier using a combination of popular image features, and then reveal its deficiency. Based on the experiments, suggestions for the design of robust camera classifiers are given.


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