A robust approach to detect digital forgeries by exploring correlation patterns

Local correlation pattern indicates integrity of an image. Exposing digital forgeries by detecting local correlation pattern of images has become an important kind of approach among many others. However, local correlation pattern is sensitive to JPEG compression, since compression can be regarded as a local homogenization and attenuates the characteristics of local correlation pattern. In this paper, rather than concentrating on the differences between image textures which is common in previous works, we specifically build a gaussian model to describe the local-correlation pattern of color filter array (CFA) interpolation. Thus the model will automatically adapt to JPEG compression. With the model built from the test image, the posterior probability map of CFA interpolation is calculated. To measure the trace of CFA, frequency characteristics of the posterior probability map are calculated and weighted combined. Then the image is classified as tampered or not by a simple threshold. Experimental results from over thousands of tampered images show the validity and efficiency of the proposed method. Moreover, we examine our algorithm to the problem of distinguishing computer-generated images from photos and detecting local tamper area. The proposed method shows a good performance in these tests.