An image copy-move forgery detection (CMFD) algorithm based on depth feature extraction and graph neural network (GNN) matching is presented to solve the problem that feature extraction is not uniform in image and single-scale superpixel segmentation has a strong impact on the result of forged location.Firstly,the image is segmented into multi-scale superpixels and the depth features are extracted.To ensure sufficient number of feature points,the uniformity of feature point distribution is calculated in the unit of superpixels,and the feature extraction threshold is reduced adaptively.Then a novel GNN feature matcher based on attention mechanism is introduced to perform iterative matching between superpixels,and random sample consensus (RANSAC) algorithm is used to eliminate mismatching.Finally,the multiscale matching results are fused to accurately locate the tampered areas.Experiments show that the proposed algorithm has good performance and the applicability of the GNN in the field of image tampering detection.