Abstract
To identify copy-move image counterfeiting, this work proposes a new feature extraction approach based on a modified Gabor filter and a Center Symmetric Local Binary Pattern (CSLBP). The input image is first pre-processed, and then Gabor filter and CSLBP feature extraction are performed to the image with various scales and orientations. The Manhattan distance is used to detect forged regions by comparing the critical spots. To classify the counterfeit photos, Hybrid Neural Networks with Decision Tree (HNN-DT) is used on feature extraction. The performance of the proposed Modified Gabor filter with CSLBP is compared to that of existing feature extraction methods such as the Improved Speeded-up Robust Features (SURF) algorithm with PCA and the existing Improved Speeded-up Robust Features (SURF) algorithm with PCA. Using the Gabor filter and CSLBP, the expected results demonstrate efficient classification.
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