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Tampering detection techniques have been continuously studied with advancements in technologies. However, many existing alternatives cannot generate object tampering results, because of the hashes attached to the image shortfall the outline information. Object tampering detection is the process of locating the modifications in the structure of an object. In this paper, we presented a method of object tampering detection which will generate the results showing the tampered images of the object. Firstly, different images of the same object are taken from different angles. Furthermore, each image of the object will then be processed to localize the region of tampering in the object. The model estimates the parameters associated with an image such as shape hash, color hash, and hash vector. Finally, a classification system, Support Vector Machine (SVM) will classify the images as tampered or not tampered based on the input fed to the classification system. This method will be useful for the product manufactures to meet the growing demands of the product as tampered products lead to non-acceptability to the end user. This technique will reduce the manpower for quality check control of the products and will identify the tampered objects from the collection.

A Hashing Based Approach to Detect Object Tampering