Journal of Lanzhou University of Technology ›› 2021, Vol. 47 ›› Issue (2): 87-96.

• Automation Technique and Computer Technology • Previous Articles     Next Articles

Copy-move image blind forgery algorithm based on quaternion convolutional neural networks with dual-tree complex wavelet

LI Ce, LI Lan, JIN Shan-gang, GAO Wei-zhe, XU Da-you   

  1. College of Electrical and Information Engineering, Lanzhou Univ. of Tech., Lanzhou 730050, China
  • Received:2020-01-09 Online:2021-04-28 Published:2021-05-11

Abstract: With the popularity of image editing software, people can forge images by Copy-move. However, existing Copy-move blind forensics algorithms are difficult to extract consistency features of color images with results depending on manually adjusting parameters, which leads to low locate accurate. Therefore, using the advantage of quaternion convolutional network on extracting spatial consistency of color images and double-tree complex wavelet on extracting image local features, Copy-move blind forensics algorithm based on quaternion convolutional network with double-tree complex wavelet is proposed. Firstly, images represented as quaternions were input to quaternion convolutional network to extract consistency features of color images, and the high-frequency subbands of double-tree complex wavelet transform were connected with feature maps of the convolutional network to learn local features. Next, similarity scores in feature vectors are calculated. Then, convolutional network is extracted features with higher scores to locate similar areas, which solves the problem of adjusting parameters during matching. Besides, an auxiliary branch is constructed to locate the tampered areas. Finally, similar and tampered areas are fused to distinguish copy and paste. The subjective and objective experiments on CoMoFoD and CASIA CMFD datasets show that the proposed algorithm improves the performance of Copy-move blind forgery.

Key words: Copy-move blind forgery, quaternion convolutional neural networks, the dual-tree complex wavelet

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