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Figure 7 Optimal chroma-like channels for a composite image.

Finally, a gradient ascent algorithm is employed to find θ∗ for the optimal chroma-like channel.

Figure 3 Edge images of spliced images in chroma-like channels.

Furthermore, the optimal chroma-like channel features even perform better than the three channels fusion features.

First, the labeled images are transformed into chroma-like channel C θ i according to (2).

(b e) The optimal chroma-like channels for GLCM, RLRN, DCT Markov, and 42DMoments, respectively.

Therefore, deriving the optimal chroma-like channel (i.e., optimal ) is equivalent to finding the largest hyperplane margin among candidate feature spaces (features extracted from chroma-like channels) which are mapped into higher spaces using gaussian kernel.

Edge images in chroma-like channels with different α,β,and γ values are given in the subsequent three rows.

The aim of optimal chroma-like channel is to find the most discriminative channel for a specific feature extraction method.

Four widely used features for image splicing detection are employed to test the effectiveness of the proposed chroma-like channel.

The diagram of the proposed framework is illustrated in Figure 4. Figure 4 Framework of optimal chroma-like channel design.