Passive Framework of Sparse Region Duplication Detection from Digital Images

Algorithm 1

Algorithm of the proposed CMFD method.

ImgIn ➔ The input image matrix

ImgGray ➔ Grayscale image

ImgBlocks ➔ A two-dimensional matrix where each block is represented by pixels

ImgFeatures ➔ Matrix representing feature space

EDist ➔ Euclidean distance between neighborhood features

D ➔ Region marker based on the threshold

ImgReconst ➔ Reconstructed image

Thresh ➔ Threshold determining if the Euclidean distance between feature spaces could mark the region

Step 1: ImgGray ← Grayscale(ImgIn) //Convert the RGB image into grayscale image

Step 2: ImgBlocks ← Divide(ImgGray,8) //divide the image into [] circular blocks

Step 3: FOREACH block in ImgBlocks

    ImgFeatures ← Extract LIOP features(block) //extract LIOP features from each circulate block

    END

Step 4: For : Size(ImgFeatures)-1

     EDist ← Euclidean(ImgFeatures[i], ImgFeatures[i+1])

      IF(EDist <= Thresh)

      D[i]=1

      ELSE

      D[i]=0

      END

    END

Step 5: ImgReconst ← Postprocessor(D)