Abstract: Region duplication forgery, in which a part of a digital image is copied and then pasted to another portion of the same image, is one of the simple and common image forgery techniques. Most of the existing algorithms are not robust to the post region duplication image processing, and have high time complexity. In this paper, we describe an efficient and robust algorithm for detecting and localizing this type of malicious tampering. The image is first reduced in dimension by Gaussian pyramid, and the Hu moment is applied to the fixed sized overlapping blocks of low-frequency image. The eigenvectors are lexicographically sorted. Then, similar eigenvectors are matched by a certain threshold value. Finally, the area threshold value is proposed to remove the wrong similar blocks. The mathematical morphology operations are performed to locate the tampered part. Experimental results show that our method is robust and that it can not only successfully detect this type of tampering for images subject to various forms of post region duplication image processing, including noise contamination, blurring, and severe lossy compression, but also reduce the total number of blocks to narrow block-matching searching space, which can improve the method efficiency.