The sensor pattern noise (SPN) can be regarded as the unique identity of a digital camera which is highly useful in digital image forensics [1, 2]. Existing methods [1, 2] which works by denoising each individual natural image often took an investigator a long time and great efforts to collect sufficient photos of diversified enough natural scenes. These processes are hard to repeat or standardized for officially using by an authority. In this work, we create noise image data set by taking photos of random noises displayed on a high definition monitor and propose a homomorphic based SPN extraction method. It offers the forensic researcher a fast way to create a large image data set in a few minutes. And the extraction method only needs to denoise once, which is highly efficient to deal with large numbers of photos. We compared the source camera identification performance of the proposed SPN extraction method to a prior state-of-art with identical experimental settings. The experimental results confirm the effectiveness of the proposed method.

Sensor pattern noise (SPN) extracted from digital images has been proved to be a unique fingerprint of digital cameras. However, SPN can be contaminated largely in the frequency domain by image content and nonunique artefacts of JPEG compression, on-sensor signal transfer, sensor design, color interpolation. The source camera identification (CI) performance based on SPN needs to be improved for small sizes of images and especially in resisting JPEG compression. Because the SPN is modelled as an additive white Gaussian noise (AWGN) in its extraction process from an image, it is reasonable to assume the camera reference SPN to be a white noise signal in order to remove the interference mentioned above. The noise residues (SPN) extracted from the original images are whitened first, then they are averaged to generate the camera reference SPN. Motivated by Goljan 's test statistic peak to correlation energy (PCE), we propose to use correlation to circular correlation norm (CCN) as the test statistic, which can lower the false positive rate to be a half of that with PCE. Theoretical analysis shows that the proposed CI method can remove the interference and raise the CCN value of a positive sample and thus achieve greater CI performance, CCN values of the negative sample class with the proposed method follow the normal distributionN(0,1) and the false positive rate can be calculated. Compared with the existing state of the art on seven cameras, 1400 photos totally (200 for each camera), the experimental results show that the proposed CI method achieves the best receiver operating characteristic (ROC) performance among all CI methods in all cases and especially achieves much better resistance to JPEG compression than all of the existing state-of-the-art CI methods.