结合关键点和块优点的复制粘贴检测算法

doi:10.16180/j.cnki.issn1007-7820.2020.03.008

Abstract

Abstract:

Digital images are widely used, and the use of various software for editing images has led to the authenticity of the images to be verified. Most of the algorithms based on keypoint detection have a limited number of key points, resulting in incomplete or unclear final detection results.This paper proposed a detection algorithm based on segmentation combined with keypoint features. First, SIFT was used to extract keypoint features and then used. The g2NN algorithm matched the key points, used the designed clustering to remove the mismatch, and according to the improved adaptive segmentation algorithm, marked the matched image blocks, and then finded the surrounding neighbor blocks to compare and matched the output results. The algorithm could effectively detected and marked the copy tampering area of large pictures, and was robust to most post-processing, and marked the tampering area more completely.

Key words: copy-and-paste detection, SIFT, segmentation, key point matching, clustering, robust

CLC Number:

TP391

YANG Shanshan,ZHANG Daxing,GUO Jiawei,WANG Shitiao. Copy-move Detection Algorithm Combining Keypoints and Block Advantages[J].Electronic Science and Technology, 2020, 33(3): 38-43.

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References 16

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	clean_walls	dark_and_bright	egyptian	red_tower
图像大小	1 024×754	2 592×3 888	3 039×2 014	3 264×2 448
运行时间	38.04 s	887.47 s	605.32 s	1117.94 s

Copy-move Detection Algorithm Combining Keypoints and Block Advantages

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