Abstract: Aiming at the problem that the excessively large number of divided sub-blocks in the traditional image copy-move forgery detection methods cause high algorithm time complexity and weak ability to resist geometric transformation, an image copy-move forgery detection algorithm based on superpixel shape features is proposed. Firstly, an adaptive division method of superpixels based on wavelet contrast is proposed to segment the image, and the stable feature points are extracted. Secondly, a novel shape coding scheme is proposed to extract superpixel shape features, which are merged with the feature points to estimate the suspected forged regions. Finally, the suspicious forged regions are segmented into superpixels again and matched to accurately locate the tampered areas. Experimental results show that the proposed method has the ability to resist geometric transformation, noise, blur and JPEG compression.

Key words: copy-move forgery detection, shape features, superpixel, feature points