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The existing splicing detection methods introduced above are usually designed for gray-scale images; however, most images on the Internet are color images and chromatic information may provide additional cues for splicing detection.
In this section, we assess and compare the performance of the order selection methods introduced above.
Besides the filter methods introduced above a number of other techniques are currently being investigated.
There are two main questions we want to answer with the methods introduced above: Do nodes that share similar network positions behave similarly in the exchange of information?
We ran all the ranking methods introduced above on the full set of testing data and plotted R (recall) and P (precision) for various values of p (on the horizontal axis).
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The behaviour of the numerical method introduced above will be illustrated with the following two examples.
In this situation, the regular SVR method introduced above cannot provide acceptable estimation results.
It is worth noticing that the method introduced above only can solve equation (26) for (x in[0,1]).
By the same method introduced above, we obtain the blow-up time (t_{-1}) and (tilde{t}_{-1}) of the related Riccati equation and the dual Riccati equation are 0.796 and 0.657, respectively.
This is in contrast to the (non-network) single-gene method introduced above in which the classification features are individual genes, but with no utilisation of network information.
The system of algebraic equations introduced above can be solved by using any standard iteration method for the unknown coefficients (c_{j}), (j=0,1,ldots,N).
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