The most common form of image forgery is copy-move, which arises when an image region is duplicated and pasted onto another region of the same image. An effective algorithm for copy-move forgery detection based on binarized statistical image features (BSIF) and principal component analysis (PCA) is presented. Initially, the suspicious image is converted to grayscale and is subsequently partitioned into overlapping blocks. Feature vectors are extracted from these blocks using BSIF, followed by dimensionality reduction using PCA. Next, as a precursor to the matching step, the feature vectors are sorted lexicographically. Additionally, a morphological opening operation is applied to eliminate outliers. This algorithm offers not just forgery detection but also the ability to localize and identify duplicated regions. The proposed algorithm was assessed using three datasets: CoMoFoD, GRIP, and UNIPA. The experimental results show that this algorithm is fast and has high accuracy for forgery detection and localization. Moreover, it has high robustness under various postprocessing operations, such as brightness, contrast adjustments, and blurring. Furthermore, the proposed algorithm outperforms some recent approaches in overall performance.