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Abstract - Essay Example

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The article “Forensic Camera Classification: Verification of Sensor Pattern Noise Approach” by Nitin Khanna, Aravind Mikkilineni and Edward Delp is an account of the evaluation of the authenticity of digital images. All the authors are engineer experts in computer,…
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The article “Forensic Camera ification: Verification of Sensor Pattern Noise Approach” by Nitin Khanna, Aravind Mikkilineni and Edward Delp is an account of the evaluation of the authenticity of digital images. All the authors are engineer experts in computer, electrical and mechanical fields. The evolution of imaging technologies is the central focus, with the role played by these technologies taking an essential position in all aspects of livelihood at individual and nations’ level. The problem of image source identification is critical to account for in forensic camera classification. The authors note Lukas and others as the basis of their experimentation. Lukas and others studied the criticality of this problem, and came up with results that sought to be confirmed through numerous experiments conducted by the aforementioned three authors of this article. To enhance the outcome of the study, different camera sets were employed, and authors of previous related works were not contacted. The verification process set off by carrying an overview of a digital camera and the images captured using them. The imaging pipeline is analyzed, with all roles performed by the lens, filters and the sensor being presented. The outcome of this process is influenced by the camera or scanner’s manufacturer, as well as pixel values attributed to the camera. Color filtering is then introduced. Array patterns and color spaces that complement the imaging pipeline is highlighted. Sensor noise is another overview variable of the digital camera. A number of noise types are outlined, including shot noise, pattern noise and readout noise. Emphasis is given to pattern noise, which is used in the source-camera identification probe. Approaches that are correlation-based are not ignored in the underlying pursuit. Comparisons of original and denoised images are used to estimate the pattern noise frequencies, especially the frequencies that are high in that regard. Classifier training and the detection scheme are the employed approaches under the correlation-based aspect of source-camera identification. Choosing the source camera is an essential action towards assessing experimental results. Reference patterns are used, and matched with high correlation to identify the source camera. Experimental thresholds can also be used, where the source camera chosen should exhibit a higher than threshold correlation value. Figures of images, cameras and scanners are used as physical frameworks of the study. The experimental results provide a list of all cameras used and the sample images that aided the study. Reference pattern was generated for the camera and the average correlation determined, taking into account desynchronization and its causes. Correlation values were obtained using unprocessed images for image identification. Figures for this process are provided for ease of understanding. A number of effects were also studied in regard to image identification. Specifically, the effects of JPEG compression, resampling and malicious processing on image identification were considered. The authors actually verified that using sensor noise and employing correlation detection can bring classification accuracy to almost 100%. This inference was in line with the findings of Lukas and others in their 2005 study. Read More
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