基于SIFT和感知哈希的图像复制粘贴篡改检测方法
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摘要
面对现在计算机软件技术的发展,图像篡改也变得轻而易举,这不仅对社会引起负面影响,甚至是难以估计的危害。针对同幅图片的复制粘贴篡改的特点,提出基于图像特征子的图像复制粘贴篡改检测方法。利用图像SIFT特征子图像进行特征向量提取,然后组成一定维度的特征向量,接着利用感知哈希对其降维,组成8位16进制的HASH值,最后利用K-means聚类定位篡改的区域。实验表明,提出的方法能够有效实现同幅图片的复制粘贴篡改检测。
Now with the development of computer software technology, image tampering has become an easy job to do, which not only caused a negative impact on the society, even it is difficult to estimate the hazard. Aiming at the characteristics of copy-and-paste tampering of the same picture, proposes a method of image copy-and-paste tampering detection based on image feature subset. The SIFT feature is used to get the eigenvector of the image, and then the eigenvector of a certain dimension are formed. Then the perceptual hash is used to reduce the dimension of the image, and the 8-bit hexadecimal Hash value is formed. Finally, the tampered area is located by K-means clustering. Experiments show that the proposed method can effectively detect the copying and pasting tampering of the same picture.
Now with the development of computer software technology, image tampering has become an easy job to do, which not only caused a negative impact on the society, even it is difficult to estimate the hazard. Aiming at the characteristics of copy-and-paste tampering of the same picture, proposes a method of image copy-and-paste tampering detection based on image feature subset. The SIFT feature is used to get the eigenvector of the image, and then the eigenvector of a certain dimension are formed. Then the perceptual hash is used to reduce the dimension of the image, and the 8-bit hexadecimal Hash value is formed. Finally, the tampered area is located by K-means clustering. Experiments show that the proposed method can effectively detect the copying and pasting tampering of the same picture.
引文
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[2]王永皎,郭力争.基于DCT变换的图像压缩技术研究[J].河南城建学院学报,2013.
[3]DCT Transform[J]. Journal of Henan Urban Construction College,2013,22(1):42-46.
[4]Lowe D G. Object Recognition from Local Scale-Invariant Features[C]. ICCV. IEEE Computer Society,1999:1150.
[5]Lowe D G. Distinctive Image Features from Scale-Invariant Keypoints[J]. International Journal of Computer Vision,2004,60(2):91-110
[6]Bay H,Ess A,Tuytelaars T,et al. Speeded-Up Robust Features[J]. Computer Vision&Image Understanding,2008,110(3):404-417.
[7]Ke Y,Sukthankar R. PCA-SIFT:a More Distinctive Representation for Local Image Descriptors[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society,2004:506-513.