This paper introduces a novel area of research to the Image Forensic field; identifying High Dynamic Range (HDR) digital images. We create a test set of images that are a combination of HDR and standard images of similar scenes. We also propose a scheme to isolate fingerprints of the HDR-induced haloing artifact at “strong” edge positions, and present experimental results in extracting suitable features for a successful SVM-driven classification of edges from HDR and standard images. A majority vote of this output is then utilised to complete a highly accurate classification system

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In this paper, we propose the novel use of Statistical Process Control (SPC) as a tool for identifying anomalies in the image acquisition process of a digital camera, for the purpose of camera identification. Control charts are used to illustrate the overall level of control associated with several devices (models include Apple iPhone 3G and 3GS, Nokia N97, and Leica D-Lux4), which are in turn reviewed in accordance with the Western Electric Rules for identifying assignable causes for the observed variation. X-Moving Range and Exponentially Weighted Moving Average (EWMA) control charts are used to highlight the variation for a subset of the devices. By implementing such a statistical model, the forensic investigator is much better positioned to understand the behaviour of a particular device, and is ultimately able to identify the most unstable feature of the cameras image acquisition process, thereby establishing a suitable fingerprint for matching images to their source