On the Performance of Li’s Unsupervised Image Classifier and the Optimal Cropping Position of Images for Forensic Investigations

Images from digital imaging devices are prevalent in society. The signatures of these images can be extracted as sensor pattern noise (SPN) and classified according to their source devices. In this paper, the authors assess the reliability of an unsupervised classifier for forensic investigation of digital images recovered from storage devices and to identify the best position for cropping the images before processing. Cross validation was performed on the classifier to assess the error rate and determine the effect of the size of the sample space and the classifier trainer on the performance of the classifier. Moreover, the authors find that the effect of saturation and subsequently the contamination of the SPN in the images affected performance negatively. To alleviate the negative performance, the authors identify the areas of images where less contamination occurs to perform cropping.</p

Instance-specific parameter tuning for meta-heuristics

Meta-heuristics are of significant interest to decision-makers due to the capability of finding good solutions for complex problems within a reasonable amount of computational time. These methods are further known to perform according to how their algorithm-specific parameters are set. As most practitioners aim for an off-the-shelf approach when using meta-heuristics, they require an easy applicable strategy to calibrate its parameters and use it. This chapter addresses the so-called Parameter Setting Problem (PSP) and presents new developments for the Instance-specific Parameter Tuning Strategy (IPTS). The IPTS presented only requires the end user to specify its preference regarding the trade-off between running time and solution quality by setting one parameter p (0 = p =1), and automatically returns a good set of algorithm-specific parameter values for each individual instance based on the calculation of a set of problem instance characteristics. The IPTS does not require any modification of the particular meta-heuristic being used. It aims to combine advantages of the Parameter Tuning Strategy (PTS) and the Parameter Control Strategy (PCS), the two major approaches to the PSP. The chapter outlines the advantages of an IPTS and shows in more detail two ways in which an IPTS can be designed. The first design approach requires expert-based knowledge of the meta-heuristic’s performance in relation to the problem at hand. The second, automated approach does not require explicit knowledge of the meta-heuristic used. Both designs use a fuzzy logic system to obtain parameter values. Results are presented for an IPTS designed to solve instances of the Travelling Salesman Problem (TSP) with the meta-heuristic Guided Local Search (GLS)