Automatic Image Splicing Detection Based on Noise Density Analysis in Raw Images

Abstract : Image splicing is a common manipulation which consists in copying part of an image in a second image. In this paper, we exploit the variation in noise characteristics in spliced images, caused by the difference in camera and lighting conditions during the image acquisition. The proposed method automatically gives a probability of alteration for any area of the image, using a local analysis of noise density. We consider both Gaussian and Poisson noise components to modelize the noise in the image. The efficiency and robustness of our method is demonstrated on a large set of images generated with an automated splicing.
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Communication dans un congrès
ACIVS-16, Oct 2016, Lecce, Italy. Springer, LNCS, International Conference of Advanced Concepts for Intelligent Vision Systems, pp.126 - 134, 2016, 17th International Conference of Advanced Concepts for Intelligent Vision Systems. 〈10.1007/978-3-319-48680-2_12〉
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Thibault Julliand, Vincent Nozick, Hugues Talbot. Automatic Image Splicing Detection Based on Noise Density Analysis in Raw Images. ACIVS-16, Oct 2016, Lecce, Italy. Springer, LNCS, International Conference of Advanced Concepts for Intelligent Vision Systems, pp.126 - 134, 2016, 17th International Conference of Advanced Concepts for Intelligent Vision Systems. 〈10.1007/978-3-319-48680-2_12〉. 〈hal-01510074〉

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