histogram-based image hashing for searching content-preserving copies

ISCAS OpenIR

	histogram-based image hashing for searching content-preserving copies
	Xiang Shijun; Kim Hyoung Joong
	2011
发表期刊	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN	0302-9743
卷号	6730 LNCS 页码:83-108
摘要	Image hashing as a compact abstract can be used for content search. Towards this end, a desired image hashing function should be resistant to those content-preserving manipulations (including additive-noise like processing and geometric deformation operations). Most countermeasures proposed in the literature usually focus on the problem of additive noises and global affine transform operations, but few are resistant to recently reported random bending attacks (RBAs). In this paper, we address an efficient and effective image hashing algorithm by using the resistance of two statistical features (image histogram in shape and mean value) for those challenging geometric deformations. Since the features are extracted from Gaussian-filtered images, the hash is also robust to common additive noise-like operations (e.g., lossy compression, low-pass filtering). The hash uniqueness is satisfactory for different sources of images. With a large number of real-world images, we construct a hash-based image search system to show that the hash function can be used for searching content-preserving copies from the same source. © 2011 Springer-Verlag Berlin Heidelberg.; Image hashing as a compact abstract can be used for content search. Towards this end, a desired image hashing function should be resistant to those content-preserving manipulations (including additive-noise like processing and geometric deformation operations). Most countermeasures proposed in the literature usually focus on the problem of additive noises and global affine transform operations, but few are resistant to recently reported random bending attacks (RBAs). In this paper, we address an efficient and effective image hashing algorithm by using the resistance of two statistical features (image histogram in shape and mean value) for those challenging geometric deformations. Since the features are extracted from Gaussian-filtered images, the hash is also robust to common additive noise-like operations (e.g., lossy compression, low-pass filtering). The hash uniqueness is satisfactory for different sources of images. With a large number of real-world images, we construct a hash-based image search system to show that the hash function can be used for searching content-preserving copies from the same source. © 2011 Springer-Verlag Berlin Heidelberg.
收录类别	EI
关键词	Additive Noise Deformation Graphic Methods Hash Functions
部门归属	(1) Department of Electronic Engineering School of Information Science and Technology Jinan University Guangzhou China; (2) State Key Laboratory of Information Security Institute of Software Chinese Academy of Sciences Beijing China; (3) CIST Graduate School of Information Management and Security Korea University Seoul Korea Republic of
语种	英语
内容类型	期刊论文
URI标识	http://ir.iscas.ac.cn/handle/311060/16154
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Xiang Shijun,Kim Hyoung Joong. histogram-based image hashing for searching content-preserving copies[J]. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),2011,6730 LNCS:83-108.
APA	Xiang Shijun,&Kim Hyoung Joong.(2011).histogram-based image hashing for searching content-preserving copies.Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),6730 LNCS,83-108.
MLA	Xiang Shijun,et al."histogram-based image hashing for searching content-preserving copies".Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6730 LNCS(2011):83-108.