Open AccessImage-Based Copy-Paste Tamper Detection Technology Based on Improved Surf
Author(s) -
Yanzhu Hu,
Yingjian Wang,
Xinbo Ai
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/612/5/052017
Subject(s) - ransac , affine transformation , artificial intelligence , computer science , robustness (evolution) , computer vision , feature (linguistics) , hessian matrix , image (mathematics) , pattern recognition (psychology) , rotation (mathematics) , pixel , feature matching , mathematics , biochemistry , chemistry , linguistics , philosophy , pure mathematics , gene
Aiming at the problem that the existing copy-paste tamper detection algorithm has poor detection effect on the image area, and the speed is slow, an image region copy-paste tampering detection algorithm based on improved SURF is proposed. The detected image extracts feature points by improving the fast Hessian matrix, and uses k-g2NN method to match the feature points, then estimates the affine transformation parameters between the matching points, and combines the R-RANSAC and SPRT to eliminate the mismatch and finally obtain the copy-paste area. The experimental results show that the proposed algorithm can accurately locate the image copy-paste tampering region, and has strong robustness to Gaussian blur, rotation and scaling operations, and the detection speed is greatly improved.