Digital images that are frequently encountered in day-to-day life can be easily tampered to mislead the information. One of the popular methods used to accomplish this unauthorized alteration is copy-move forgery. This paper presents a passive authentication scheme for copy-move forgery which uses discrete cosine transform (DCT) with block-wise peak-pixel-based block clustering. This scheme initially aims to obtain the features by implementing DCT on small, fixed image blocks and minimizes the size of feature vectors. The block-wise peak-pixel-based block clustering algorithm is used instead of the general lexicographic order technologies to enhance the detection precision. By comparing the feature vectors in each bucket, similar blocks will be obtained. Based on the experimental outcomes, the proposed scheme can detect multiple irregular and significant tampered regions. The duplicated regions detected in the distorted digital images can also be displayed by adding white Gaussian noise, Gaussian blurring and their mixed operations. By applying the above approach to the CoMoFoD – Image Database an accuracy of 99.99% has been achieved.