In this paper, a novel copy-move forgery detection (CMFD) method for the digital image using the histogram and GLCM-based rotation-invariant feature descriptor is proposed. In developing an efficient CMFD method, there are two fundamental challenges needed to be addressed: accuracy and processing time. To achieve this goal, a fast and straightforward histogram-based, and GLCM-based local features are combined to increase the uniqueness and accuracy of the detection while also maintaining the computational cost, resulting in relatively fast detection mechanism suitable for practical use. The detection mechanism, firstly, performs keypoint detection using SURF-based keypoint detection method. The local GLCM-based and histogram-based features for each block are then calculated and combined using the convolution method. All generated features are then sorted and compared. Finally, lines between matched features are drawn to express the relationship between the original and copy-move regions. Experimental results show that the proposed method outperforms some traditional methods in term of accuracy while also greatly reduces the computational complexity of the system compared to some existing techniques.