In the reform of criminal procedure system centered on trial, the examination and acceptance of image evidence is particularly important. The most common operation of digital image tampering is copy and paste, by copying a region of the image paste to other parts, in order to increase or cover up part of the image content, accompanied by geometric transformation to increase the authenticity. Most of the existing algorithms for detecting similar forgeries can only deal with a certain geometric transformation, which has high computational complexity and cannot deal with the situation of multi-region copy and paste. A method of extracting scale-invariant features based on color and texture information is proposed. At first, the key points are extracted by detecting the local invariant features in the color channel of the image, and the hierarchical clustering algorithm is used for clustering. And then, the affine transformation estimation is carried out on the matching results of the key points. At last, the random sampling consensus algorithm is used to remove the unmatched key points. Experimental results show that the algorithm can better deal with the multi-region copy-paste tamper detection after a variety of transformations, and can play a role in the public security image inspection and identification work.