Enhancing ROC performance of trustworthy camera source identification

Abstract

Sensor pattern noise (SPN) extracted from digital images has been proved to be a unique fingerprint of digital camera. However, sensor pattern noise can be contaminated largely in frequency domain by image detail from scene according to Li's work and non-unique artifacts of on-sensor signal transfer, sensor design, color interpolation according to Chen et al's work, the source camera identification performance based on SPN needs to be improved especially for small image block. Motivated by their works, in order to lessen the effect of these contaminations, the unique SPN fingerprint for identifying one specific camera is assumed to be a white noise which has a flat frequency spectrum, so the SPN extracted from an image is whitened first to have a flat frequency spectrum, then inputted to the mixed correlation detector. Source camera identification is the detection of the existence of the camera reference SPN in the SPN extracted from a single image. Compared with the correlation detection approach and Li's model based approaches on 7 cameras, 1400 photos totally, each camera is responsible for 200, the experimental results show that the proposed mixed correlation detection enhances the receiver operating characteristic (ROC) performance of source camera identification, especially greatly raises the detection rate (true positive rate) in the case of trustworthy identification which is with a low false positive rate. For example, the proposed mixed correlation detection raises the true positive rate from 78% to 93% at zero false positive rate on image blocks of 256x256 pixels cropped from the center of the 1400 photos. The proposed mixed correlation detection also has large advantage to resist JPEG compression with low quality factor. Fridrich's group has proposed two reference SPN extraction methods which are the noise residues averaging and the maximum likelihood estimation method. They are compared from the aspect of ROC performance associated with the correlation detection and mixed correlation detection respectively. It is observed that the combination of mixed correlation detection and reference SPN extraction method of noise residues averaging achieves the best performance. We also demonstrate an image management application of the proposed SPN detection method for the news agent. It shows that the detection method discriminates the positive samples from a large number of negative samples very well on image bock size of 512×512 pixels.