Abstract: Aiming at the problems of high algorithm time complexity and incomplete location region in image copy-move forgery detection, an image copy-move forgery detection algorithm based on deep features extraction and discrete cosine transform is proposed. Firstly, the color and texture information of the image are fused to obtain the four-channel image, the adaptive feature extraction threshold is calculated, and the depth feature is extracted by the feature detector based on the full convolutional neural network. Secondly, the discrete cosine transform is used to extract block features for preliminary matching, and point feature vectors are adopted to eliminate mismatches. Finally, the tampered regions are accurately located by convolution operation. The verification on the public dataset fully demonstrates the advantages of the algorithm in the detection efficiency and the integrity of the positioning area. 

Key words: copy-move forgery detection, deep feature, discrete cosine transform, convolution operation