Preprocessing Reference Sensor Pattern Noise via Spectrum Equalization

Although sensor pattern noise (SPN) has been proved to be an effective means to uniquely identify digital cameras, some non-unique artifacts, shared among cameras undergo the same or similar in-camera processing procedures, often give rise to false identifications. Therefore, it is desirable and necessary to suppress these unwanted artifacts so as to improve the accuracy and reliability. In this paper, we propose a novel preprocessing approach for attenuating the influence of the non-unique artifacts on the reference SPN to reduce the false identification rate. Specifically, we equalize the magnitude spectrum of the reference SPN through detecting and suppressing the peaks according to the local characteristics, aiming at removing the interfering periodic artifacts. Combined with six SPN extractions or enhancement methods, our proposed spectrum equalization algorithm is evaluated on the Dresden image database as well as our own database, and compared with the state-of-the-art preprocessing schemes. The experimental results indicate that the proposed procedure outperforms, or at least performs comparable with, the existing methods in terms of the overall receiver operating characteristic curves and kappa statistic computed from a confusion matrix, and tends to be more resistant to JPEG compression for medium and small image blocks.