Forensic sensor pattern noise extraction from large image data set

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.