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 mod-elize 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.