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| histogram-based image hashing for searching content-preserving copies | |
| Xiang Shijun; Kim Hyoung Joong | |
| 2011 | |
| Source | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
 |
| ISSN | 0302-9743 |
| Volume | 6730 LNCSPages:83-108 |
| English Abstract | 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. |
| Indexed Type | EI |
| Keyword | Additive Noise Deformation Graphic Methods Hash Functions |
| Department | (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 |
| Language | 英语 |
| Content Type | 期刊论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/16154 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation 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. |
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