Due to the advanced multimedia editing tools and supported by sophisticated hardware, creating image/video manipulations for malicious purposes is increasing which is almost impossible to detect manually. Moreover, to conceal the traces, different post-processing operations are performed. Therefore, authenticity is a growing concern and important for identifying original and forged images. One of the popular image manipulations is copy-move forgery in which one or more regions in the image are duplicated to create a malicious effect within an image. The work in this article presents redundant wavelet transform based complex Zernike moment and Scale Invariant Feature Transform (SIFT) keypoint matching technique for copy-move image forgery operation detection. SIFT is robust against scale and rotation that works on identifying similarity using exhaustive search of SIFT features. After extracting SIFT keypoint features agglomerative hierarchical clustering is employed for grouping and key point matching operation is performed. Finally, block matching operation is evaluated and forged regions in the manipulated images are marked and displayed. This work also presents optimized SIFT key-point feature computations resulting in lower computation time, often one of the requirements in real time deployment. The proposed algorithm performance is evaluated using Precision, Recall, F-Measure and average detection accuracy on popular and publicly available MICC-220 database. The proposed technique demonstrates improved speedup and detection rate compared to existing approaches.