Predicting and Classifying User Identification Code System Based on Support Vector Machines

In digital fingerprinting, preventing piracy of images by colluders is an important and tedious issue. Each image will be embedded with a unique User IDentification (UID) code that is the fingerprint for tracking the authorized user. The proposed hiding scheme makes use of a random number generator to scramble two copies of a UID, which will then be hidden in the randomly selected medium frequency coefficients of the host image. The linear support vector machine (SVM) will be used to train classifications by calculating the normalized correlation (NC) for the 2-class UID codes. The trained classifications will be the models used for identifying unreadable UID codes. Experimental results showed that the success of predicting the unreadable UID codes can be increased by applying SVM. The proposed scheme can be used to provide protections to intellectual property rights of digital images and to keep track of users to prevent collaborative piracies.