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Image tampering detection based on a statistical model

Abstract : This paper presents a novel method for image manipulation iden- tification of natural images in JPEG format. The image forgery detection technique is based on a signal-dependent noise model that is relevant to de- scribe a natural image acquired by a digital camera. This parametric model is characterized by two fingerprints which are used to falsification identification. The problem is cast in the framework of the hypothesis testing theory. For practical use, the Generalized Likelihood Ratio Tests (GLRT) are presented and their performance is theoretically established. There are different types of image forgery which have been considered in this paper for example re- sampling, Gaussian filtering and median filtering. Experiments with real and simulated images highlight the relevance of the proposed approach.
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Contributor : Jean-Baptiste VU VAN Connect in order to contact the contributor
Submitted on : Monday, August 16, 2021 - 2:19:14 PM
Last modification on : Sunday, June 26, 2022 - 9:27:02 AM




Thi Ngoc Canh Doan, Florent Retraint, Cathel Zitzmann. Image tampering detection based on a statistical model. Multimedia Tools and Applications, Springer Verlag, inPress, ⟨10.1007/s11042-021-11213-3⟩. ⟨hal-03320795⟩



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