Aiming at the disadvantage that the existing detection algorithms are difficult to resist combined attacks, a copy-move forgery recognition algorithm based on mixed features is proposed. Different from the traditional algorithm using fixed threshold, the proposed algorithm uses the similar sub-block extraction method without threshold to select the sub-block with high correlation. At the same time, in order to obtain more local information, an adaptive sub-block synthesis scheme is proposed to avoid sub-block aliasing. In addition, aiming at the problem that scale-invariant feature transform (SIFT) features cannot distinguish natural similar regions from tampered regions, the proposed algorithm combines the advantages of moment features to extract the progressive hybrid features of synthetic sub-blocks, so as to reduce the false alarm rate of the algorithm. The experimental results show that the true positive rate (TPR) and F1 of the proposed algorithm are 97.2% and 92.9% on MICC-F2000 data set and 98.2% and 95.1% on MICC-F220 data set, respectively, indicating that the proposed algorithm has good detection ability.