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A NOVEL REPLICA DETECTION SYSTEM USING BINARY CLASSIFIERS, R-TREES, AND PCA - Printable Version +- Free Academic Seminars And Projects Reports (https://easyreport.in) +-- Forum: Seminars Topics And Discussions (https://easyreport.in/forumdisplay.php?fid=30) +--- Forum: Engineering Seminars Topics (https://easyreport.in/forumdisplay.php?fid=7) +---- Forum: Computer Science Seminar Topics (https://easyreport.in/forumdisplay.php?fid=12) +---- Thread: A NOVEL REPLICA DETECTION SYSTEM USING BINARY CLASSIFIERS, R-TREES, AND PCA (/showthread.php?tid=40354) |
A NOVEL REPLICA DETECTION SYSTEM USING BINARY CLASSIFIERS, R-TREES, AND PCA - sameee - 10-04-2017 A NOVEL REPLICA DETECTION SYSTEM USING BINARY CLASSIFIERS, R-TREES, AND PCA [attachment=475] ABSTRACT Replica detection is a prerequisite for the discovery of copyright infringement and detection of illicit content. For this purpose, contentbased systems can be an efficient alternative to watermarking. Rather than imperceptibly embedding a signal, content-based systems rely on image similarity. Certain content-based systems use adaptive classifiers to detect replicas. In such systems, a suspect image is tested against every original, which can become computationally prohibitive as the number of original images grows. INTRODUCTION The recent progress in multimedia technologies and the advent of the WorldWideWeb (Web) have permitted to process and distribute digital content at negligible costs. Unfortunately, many valuable digital images are now illegally redistributed. In this context, both content protection and detection of copyright infringements becomes important. In this paper, we propose a system to detect image replicas. By the term replica, we refer not only to a bit exact copy of a given original image, but also to modified versions of the image after certain manipulations, malicious or not, as long as these manipulations do not change the perceptual meaning of the image content. In particular, replicas include all variants of the original image obtained after common image processing manipulations such as compression, filtering, adjustments of contrast, or geometric manipulations. REPLICA DETECTION SYSTEM The main idea behind the proposed replica detection system is to use a binary classifier to determine whether the suspect image is a replica of an image contained in a database of originals. Although the number of originals is quite small compared to that of all images on the Web, it can still be fairly large depending on the application (for example in the thousands or even millions). When using a set of binary classifiers, each being able to detect whether a suspect image is a replica of a specific image in the database, the entire database has to be sequentially scanned, which becomes quickly cumbersome as the number of originals grows. Therefore, we propose to use a preprocessing step based on an indexing structure where, given a suspect image, the most likely original images are efficiently selected. We denote the set of likely originals, or candidates, C. Ideally, C contains few elements and, includes the correct original if the suspect image is indeed a replica of one of the images in the database. R-tree Performance The R-tree performance is assessed by measuring the miss-rate (i.e. the average probability that the R-tree does not return among its results the corresponding original when the test image is a replica) and the average number of returned candidates. For this purpose, the subsets Q and S (of Q and S respectively), that do not include the non-replica images, are used. Note that in general, the average number of returned candidates for non-replica images is one less than for replica images. CONCLUSION In this work, a replica detection system capable of retrieving from a database of originals the one that corresponds to a given suspect image was presented. Since binary classifiers are used by the system, the suspect image has to be tested against every original contained in the database. |