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Volume: 29 | Article ID: art00016
Linear Filter Kernel Estimation Based on Digital Camera Sensor Noise
  DOI :  10.2352/ISSN.2470-1173.2017.7.MWSF-332  Published OnlineJanuary 2017

We study linear filter kernel estimation from processed digital images under the assumption that the image's source camera is known. By leveraging easy-to-obtain camera-specific sensor noise fingerprints as a proxy, we have identified the linear crosscorrelation between a pre-computed camera fingerprint estimate and a noise residual extracted from the filtered query image as a viable domain to perform filter estimation. The result is a simple yet accurate filter kernel estimation technique that is relatively independent of image content and that does not rely on hand-crafted parameter settings. Experimental results obtained from both uncompressed and JPEG compressed images suggests performances on par with highly developed iterative constrained minimization techniques.

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Chang Liu, Matthias Kirchner, "Linear Filter Kernel Estimation Based on Digital Camera Sensor Noisein Proc. IS&T Int’l. Symp. on Electronic Imaging: Media Watermarking, Security, and Forensics,  2017,  pp 104 - 112,

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