In this paper, we propose a two-pass hashing feature representation and searching method for copy-move forgery detection with a good score and high efficiency. First, the normalized moment transformation is presented to extract the corresponding block features from multiple frequency images. The multiple-dimensional features of each pixel are projected into the corresponding hashing bin to obtain the corresponding hashing features. Then, a novel two-pass hashing feature representation is proposed to concatenate multiple hashing features as the bit sequence. Based on the two-pass hashing feature representations, the two-pass hashing searching algorithm searches and updates the nearest pixel matches in high efficiency. Finally, post-processing operations are proposed to accurately identify the forgery regions. The experimental results show that the proposed copy-move forgery detection method can achieve the best scores among the state-of-the-art methods, even under various attacks. In addition, the proposed method has a very high detection efficiency without iterations.