Perceptual Image Hashing Based on Shape Contexts and Local Feature Points

Lv, Xudong; Wang, Z. J.

doi:10.1109/tifs.2012.2190594

Cited by 157 publications

(69 citation statements)

References 32 publications

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“…Radon transform (RT) is used to estimate this information [21]. The estimation process of θ C using RT is as follows:…”

Section: The Central Orientation (θ C ) Estimation and Image Restorationmentioning

confidence: 99%

“…Compressive sensing (CS) [19], matrix factorization [20], feature point [21], moments [3,22], and ring partition [2,23] have also been used to develop image hashing techniques. The image hash construction by using compressive sensing has been proposed by Kang et al [19].…”

Section: Introductionmentioning

confidence: 99%

“…This method is robust to some content-preserving operation, but variant for rotation of large degree. Lv and Wang [21] has proposed a hashing technique using scale invariant feature transform (SIFT). In this method, keys are detected by SIFT, and only stable keys are selected based on Harris criterion.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robust image hashing through DWT-SVD and spectral residual method

Karsh

Laskar

Aditi

2017

J Image Video Proc.

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In the last few decades, the discovery of various methods for generating secure image hash has become revolutionary in the field of image hashing. This paper presents an efficient approach to obtain image hash through DWT-SVD and a saliency detection technique using spectral residual model. The latest image hashing technique based on ring partition and invariant vector distance is rotation invariant for the large angle at the cost of being insensitive to corner forgery. But, due to the use of the central orientation information, the proposed system is rotation invariant for arbitrary angles along with sensitiveness to corner changes. In addition, we have used the HSV color space that gives desirable classification performance. It provides satisfactory results against large degree rotation, JPEG compression, brightness and contrast adjustment, watermarking, etc. This technique is also sensitive to malicious activities. Moreover, it locates the forged areas of a forged image. We have applied the proposed algorithm to a large collection of images from various databases. The receiver operating characteristics shows that the proposed method is better than some state-of-the-art methods.

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“…Radon transform (RT) is used to estimate this information [21]. The estimation process of θ C using RT is as follows:…”

Section: The Central Orientation (θ C ) Estimation and Image Restorationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust image hashing through DWT-SVD and spectral residual method

Karsh

Laskar

Aditi

2017

J Image Video Proc.

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“…A proper subset of moments is always the best choice that describes the exact content of image. Zernike moments have an orthogonal basis functions and are used as Global image features in most authentication system [2].Lv et al [12] explained about local feature points and shape context by using SIFT as feature extraction method. SIFT is used to select the most stable key points from the image and it is embedded to generate image hash.…”

Section: Introductionmentioning

confidence: 99%

Combining Image Security and Authentication Using Sift and Zernike Moments

Kanimozhi¹,

Dr.G.²

2018

BIJNTA

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“…At the same time, in the case of a large rotation, light, partial occlusion and random noise, the results obtained by an image are not good. While based on feature points, including Harris operator [6], [7], SIFT feature point, SURF feature point and so on, the matching method whose performance is depended largely on the quality of extraction features can better solve such problems. In the current feature point matching algorithms, most detection methods of feature points are to consider the n × n square neighborhood.…”

Section: Introductionmentioning

confidence: 99%

A Method of Enhanced Corner Points Extraction Based on Circular Neighborhood

Jiang¹,

Yu²,

Zhou³

2014

IJCEE

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Abstract-In this paper, it presents an improved Harris feature extraction algorithm, which is based on Harris algorithm to extract candidate feature points and screen angular points that satisfy certain constraints on the edge of circular neighborhood for the determination of final feature points. And then it makes use of Gaussian-weighted to establish a proximity matrix between internal feature points in two images and uses the singular value decomposition to get their eigenvectors. At last it makes use of eigenvectors to create a relation matrix to reflect the matching degree between feature points for matching. And it shows that extracted feature points in the proposed method in the experiments is robust strongly for rotation, illumination, noise, etc., with good matching effect, low rate of false matches.Index Terms-Harris, the edge of circular neighborhood, singular value decomposition.

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Perceptual Image Hashing Based on Shape Contexts and Local Feature Points

Cited by 157 publications

References 32 publications

Robust image hashing through DWT-SVD and spectral residual method

Robust image hashing through DWT-SVD and spectral residual method

Combining Image Security and Authentication Using Sift and Zernike Moments

A Method of Enhanced Corner Points Extraction Based on Circular Neighborhood

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