In this paper we propose the use of a Bayesian framework to allow characterisation of image tampering from a library of attacks. We use the double watermarking strategy proposed in our previous work to derive sufficient information to drive the classifier. A non-parametric Bayesian classifier, trained on data derived from Monte Carlo simulations is used. In addition to classification, the effects of varying the input parameters are studied. The results obtained show that the non-parametric Bayesian classifier has a very low misclassification rate for this type of problem. Explanations as to the nature of the results, and some of the practical considerations, are given.
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