A passive multi-purpose scheme based on periodicity analysis of CFA artifacts for image forensics

• A passive-blind multi-purpose scheme for photographic image detection and device class identification.
• The accuracy rate of our scheme on 5805 test images is 95.56%, which is higher than those of the existing methods proposed in [11] and [12].
• For device class identification, the precision rate of our method is at least 93% on Canon, Sony, and Nikon images.

We propose a passive multi-purpose scheme for photographic image (PIM) detection and a device class identification method. The motivation for the scheme is the periodicity phenomenon caused by color filter arrays (CFAs) and the demosaicing process. The phenomenon only occurs in the Fourier spectrum in PIMs. The proposed scheme exploits prediction error statistics, local peak detection, and a PIM classifier to analyze the phenomenon for PIM detection. We also develop a hierarchical classifier for device class identification based on the analysis of local peaks in the Fourier spectrum.To evaluate the scheme’s performance, we compile a test dataset of PIMs and PRCG (photorealistic computer graphics) images, and analyze the impact of leak peak detection, JPEG lossy compression, and cropping operations on PIM detection. The accuracy rate of the scheme on 5805 test images is 95.56%, which is higher than that of the methods proposed in Sutthiwan et al. (2009) [1] and Gallagher and Chen (2008) [2]. In addition, for device class identification, the precision rate of the proposed method is at least 93% on Canon, Sony, and Nikon images. The experiment results demonstrate the efficacy of the proposed multi-purpose scheme.