At present, many image duplicationpaste tampering detection algorithms mainly rely on the gray level information of the image to detect image features, but do not consider the color factor of the image, resulting in the deficiency of error detection and missing detection in the detection results. Based on cosine modulated Gauss filtering, an image copypaste tampering detection algorithm based on color metric factor coupled with local feature clustering is designed in this paper. The CMG is used to obtain the scale response value of the image, and the candidate feature points are extracted by the extremum calculation. The spectral reflection model of the pixels is used to establish the color measurement factor, which is used to determine the image feature points from the candidate feature points. The neighborhood circle of the feature points is constructed and the quaternion exponential moments in the circle are obtained to form the feature vectors. The Euclidean distance between feature points is calculated by using eigenvectors to complete image feature matching. By using the R, G and B values of matching point pairs, the local features of feature points are formed, the clustering of image features is completed, the forgery content is located and copied and pasted, and the tampering detection results are obtained. The simulation results show that compared with the current copypaste forgery detection method, the proposed method has higher detection accuracy and robustness for simple copypaste forgery and complex combination forgery.