Manuscript received October 18, 2022; revised November 28, 2022, accepted December 11, 2022.

Abstract—With advancement in technology, especially in imaging field, digital image forgery has increased a lot nowadays. In order to counter this problem, many forgery detection techniques have been developed from time to time. For rapid and accurate detection of forged image, a novel hybrid technique is used in this research work that implements Gray Level Co-occurrence Matrix (GLCM) along with Binary Robust Invariant Scalable Keypoints (BRISK). GLCM significantly extracts key attributes from an image efficiently which will help to increase the detection accuracy. BRISK is known to be one of the 3 fastest modes of detection which will increase the execution speed of GLCM. BRISK even processes scaled and rotated images. Then the Principal Component Analysis (PCA) algorithm is applied in the final phase of detection will remove any unrequited element from the scene and highlights the concerned forged area.

Keywords—copy-move forgery, Discrete Wavelet Transform (DWT), Gray Level Co-occurrence Matrix (GLCM), general architecture, image manipulation

Cite: Amarpreet Singh and Sanjogdeep Singh, "Gray Level Co-occurrence Matrix with Binary Robust Invariant Scalable Keypoints for Detecting Copy Move Forgeries," Journal of Image and Graphics, Vol. 11, No. 1, pp. 82-90, March 2023.

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