Demosaicking recognition with applications in digital photo authentication based on a quadratic pixel correlation model

Most digital still color cameras use a single electronic sensor (CCD or CMOS) overlaid with a color filter array. At each pixel location only one color sample is taken, and the other colors must be interpolated using neighboring samples. This color plane interpolation is known as demosaicking, which is one of the important tasks in a digital camera pipeline. Demosaicked images possess spatially periodic inter-pixel correlation. In this paper, such correlation is expressed in a quadratic form, and Principal Component Analysis is applied to filter out intrinsic scene correlation. A decision mechanism using BP neural networks and a majority-voting scheme is designed to recognize demosaicking correlation and authenticate digital photos. Experiments show that, the proposed method can accurately classify images by demosaicking algorithms or source cameras, and it is effective to detect rendering forgeries. The sensitivity and robustness of the method are also verified. This algorithm-independent approach is especially useful when demosaicking algorithm is only available in form of binary code or integrated circuit without technical detail.