Patch-based sensor pattern noise for camera source identification
Sensor pattern noise (SPN) has been proved to be an inherent fingerprint of a camera, and it has been broadly used in the fields of image authentication and camera source identification. However, the SPN extracted using current denoising algorithm always contains image content residual, which would significatively influence the accuracy of camera source identification. In this paper, a novel patch-based (PB) sensor pattern noise algorithm for camera source identification is proposed to solve this problem. Low-complexity patches of images are selected to construct local reference SPN, which contains least image content residual. The global reference SPN is constituted with the block-wised local SPN. Similarly for the test image, SPN is extracted from low-complexity region, and making correlation with corresponding local reference SPN. Our experiments on the Dresden database demonstrate that the proposed approach outperforms two sensor pattern noise estimation methods on the literatures as baseline.