Robust Hashing for Image Authentication Using Zernike Moments and Local Features

BackgroundImage authentication is one of the challenging research areas in the multimedia technology due to the availability of image editing tools. Image hash may be used for image authentication which should be invariant to perceptually similar image and sensitive to content changes. The challenging issue in image hashing is to design a system which simultaneously provides rotation robustness, desirable discrimination, sensitivity and localization of forged area with minimum hash length.MethodsIn this paper, a perceptually robust image hashing technique based on global and local features has been proposed. The Global feature was extracted using ring partition and projected gradient nonnegative matrix factorization (PGNMF). The ring partitioning technique converts a square image into a secondary image that makes the system rotation invariant. The PGNMF which is usually faster than the other NMFs has been used to reduce the dimension of the secondary image to generate the shorter hash sequence. The local features extracted from the salient regions of the image help to localize the forged region in the maliciously manipulated images. The image hashing techniques that use only global features are limited in discrimination.ResultsThe experimental results reveal that the proposed image hashing method based on global and local features provides better discrimination capability. The proposed hashing method is tested on large image sets collected from the different standard database. It is observed from the experimental results that the proposed system is robust to content-preserving operations and is capable of localizing the counterfeit area.ConclusionsThe combination of global and local features is robust against the content-preserving operations, which has a desirable discriminative capability. The proposed system may be used in image authentication, forensic evidence, and image retrieval, etc.

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“…The salient region of an image can be obtained by taking the threshold three times of the mean of the (Zhao et al 2013). …”

Section: Methodsmentioning

confidence: 99%

“…It can also identify the counterfeit area. An image hashing method with the combination of global and local features has been proposed in the literature (Zhao et al 2013). These features are based on Zernike moments and shape-texture respectively.…”

Section: Introductionmentioning

confidence: 99%

Robust image hashing using ring partition-PGNMF and local features

2016

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“…Subsequently, research on the theory and application of image hashes quickly raised concerns. Many algorithms for generating image hashes have also been proposed [37][38][39]. In pictures, high frequencies provide detailed information, whereas low frequencies reveal structures.…”

Section: Degree Of Global Feature Consistencymentioning

confidence: 99%

A Hierarchical Approach for Measuring the Consistency of Water Areas between Multiple Representations of Tile Maps with Different Scales

Shen

2017

IJGI

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Abstract:In geographic information systems, the reliability of querying, analysing, or reasoning results depends on the data quality. One central criterion of data quality is consistency, and identifying inconsistencies is crucial for maintaining the integrity of spatial data from multiple sources or at multiple resolutions. In traditional methods of consistency assessment, vector data are used as the primary experimental data. In this manuscript, we describe the use of a new type of raster data, tile maps, to access the consistency of information from multiscale representations of the water bodies that make up drainage systems. We describe a hierarchical methodology to determine the spatial consistency of tile-map datasets that display water areas in a raster format. Three characteristic indices, the degree of global feature consistency, the degree of local feature consistency, and the degree of overlap, are proposed to measure the consistency of multiscale representations of water areas. The perceptual hash algorithm and the scale-invariant feature transform (SIFT) descriptor are applied to extract and measure the global and local features of water areas. By performing combined calculations using these three characteristic indices, the degrees of consistency of multiscale representations of water areas can be divided into five grades: exactly consistent, highly consistent, moderately consistent, less consistent, and inconsistent. For evaluation purposes, the proposed method is applied to several test areas from the Tiandi map of China. In addition, we identify key technologies that are related to the process of extracting water areas from a tile map. The accuracy of the consistency assessment method is evaluated, and our experimental results confirm that the proposed methodology is efficient and accurate.

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“…Zhao, Wang and Zhang [20] proposed a method that also combines the local features and the global features for image hashing, but the method cannot resist high strength noise addition for the reason that the noise may influence the extraction of the salient regions. And the method is not robust against cropping and rotation distortion with large angle.…”

Section: Performance Comparisonsmentioning

confidence: 99%

“…Zhao et al propose some hashing methods that combine the local texture features and Zernike moments for image hashing generation, which can't resist high strength noise addition and some geometric attacks, e.g. cropping [19], [20]. Tang et al describe some robust hashing methods based on the invariant features.…”

Section: Introductionmentioning

confidence: 99%