摘要
为减少图像伪造检测的计算复杂度并提高精度,设计基于扩展块与随机样本一致性的建筑图像伪造检测方案。利用建筑图像轮廓,通过块扩展技术提取建筑图像特征;将图像划分为相同尺寸的重叠块,提取每个块的显著特征;根据得到的特征对块进行分组,将组中相邻3个块组合成一个储存区;通过构建一个连接矩阵,将不符合要求的子块删除;从储存区剩余的块中,计算块的总面积,检测伪造子块;引入RANSCA(random sample consensus algorithm),对匹配结果进行优化。实验结果表明,与当前图像伪造检测方案相比,提出的方案的精度与效率更高,对模糊、噪声、JPEG压缩等伪造具有更强的检测性能。
To reduce the complexity of building image forgery detection and improve the detection accuracy,an image forgery detection algorithm based on the extended block coupling random sample consensus was proposed.The image feature was extracted from the building image contour using the block expansion technique.The image was divided into overlapping blocks with same size,and the salient features of each block were extracted.The blocks were grouped according to the obtained features,and three adjacent blocks were combined into a storage area.The block in the storage area was constructed as a connection matrix to calculate the test statistic of blocks in the storage area to remove these blocks that failed to meet the requirements.From the remaining blocks in the storage area,the total area was calculated to detect the forgery sub block.The RANSCA(random sample consensus algorithm,RANSCA)was introduced to improve the matching accuracy.Experimental results show that compared with the current image forgery detection algorithm,the proposed algorithm improves the computational efficiency and the detection accuracy,and has good detection performance for fuzzy,noise and JPEG compression.
作者
程伟
孟聪龄
李业学
CHENG Wei;MENG Cong-ling;LI Ye-xue(College of Architectural Engineering, Hubei University of Arts and Science, Xiangyang 441053, China;College of Civil and Structural Engineering, Taiyuan University of Technology, Taiyuan 250104, China)
出处
《计算机工程与设计》
北大核心
2018年第5期1422-1427,1438,共7页
Computer Engineering and Design
基金
国家自然科学基金项目(51374100)
湖北省教育厅科学技术研究计划重点基金项目(D20142603)
湖北省教育厅研究基金项目(15Q202)
湖北省自然科学基金项目(2013CFC029)
关键词
建筑图像
伪造检测
扩展块
连接矩阵
储存区
成角透视原理
随机样本一致性
architectural image
forgery detection
extension block
connection matrix
storage area
perspective principle
random sample consistency