Robust and Secure Image Hashing via Non-Negative Matrix Factorizations

Monga, Vishal; Mhcak, M.K.

doi:10.1109/tifs.2007.902670

Cited by 265 publications

(154 citation statements)

References 18 publications

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“…Hence, the feature extraction stage is significantly important in order to achieve robustness, discrimination and security. Some of the robust feature extraction methods exploited in literature include image histogram [10], feature points [11] or image edge information, significant DWT or DCT coefficients, Fourier transform [8], and dimensionality reduction with linear transforms [12].…”

Section: General Framework Of Image Hashingmentioning

confidence: 99%

“…Therefore, keybased randomization should be incorporated into feature extraction to make the hash unpredictable and resist these threats. Another merit of randomization stated in [12] is in enhancing the scalability of the hash algorithm, i.e., the ability to work with large data sets while avoiding the collision for distinct inputs. This step also ensures source authentication of image data.…”

Section: General Framework Of Image Hashingmentioning

confidence: 99%

“…Furthermore, a key-based permutation can be applied to hash vectors for randomization [10]. However, the security is not as strong as in previous feature extraction stage, also the accuracy will be varied with the changes of key inserting [12]. The binary representation of hash vectors is expected to use Gray Code [14] in order to avoid the bit error caused by natural binary code.…”

Section: General Framework Of Image Hashingmentioning

confidence: 99%

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A Survey on Image Hashing for Image Authentication

Rhee

2010

IEICE Trans. Inf. & Syst.

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SUMMARYThe traditional cryptographic hash functions are sensitive to even one-bit difference of the input message. While multimedia data always undergo compression or other signal processing operations, which lead to the unsuitability of multimedia authentication using cryptographic hash. The image hashing has emerged recently which captures visual essentials for robust image authentication. In this paper, we give a comprehensive survey of image hashing. We present an overview of various image hashing schemes and discuss their advantages and limitations in terms of security, robustness, and discrimination under different types of operations on the image.

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Section: General Framework Of Image Hashingmentioning

confidence: 99%

Section: General Framework Of Image Hashingmentioning

confidence: 99%

Section: General Framework Of Image Hashingmentioning

confidence: 99%

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A Survey on Image Hashing for Image Authentication

Rhee

2010

IEICE Trans. Inf. & Syst.

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“…But the current research of image perceptual hashing algorithm is short of thinking about human visual properties. Most of the existing hashing algorithms [5,6] consider only the gray images. On the other hand, many hashing algorithms are sensitive to rotation.…”

Section: Introductionmentioning

confidence: 99%

The Robust Algorithm of 3D Medical Image Retrieval Based on Perceptual Hashing

Xu¹,

Li²,

Huang³

2015

Proceedings of the 2015 International Conference on Mechatronics, Electronic, Industrial and Control Engineering

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Abstract-In order to deal with the difficulties of the heavy workloads and ambiguity of text labeling in the traditional image retrieval and the problem that the studies on 3D medical images retrieval were lacking, this paper proposes an robust algorithm based on perceptual hashing for 3D medical images retrieval. At first, the hash value of 3D medical image to be retrieved is extracted through perceptual hashing. And then the hash value database is established. Next, the NC(Normalized Cross Correlation Coefficient, NC) between the hash value of the image to be retrieved and each one in the hash value database is computed automatically. Finally the corresponding image with the highest NC value is retrieved and the 3D medical image retrieval is realized. The results show that this algorithm can distinguish different 3D images remarkably and has ideal robustness against Guassian noise, JPEG compression, Median filtering attacks, which is obviously better than other algorithms based on DCT, DFT. In addition, this algorithm has rapid retrieval capability and good practicability.

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“…The crypto- graphic hash functions cannot be used on digital multimedia because they are very sensitive to every bit of digital data. Many researchers have presented robust and secure hash functions for images [20], [21], videos [22], [23], and 3D models [24]- [26]. However, these functions cannot be used for vector data models because the data structure for the latter is different.…”

Section: Introductionmentioning

confidence: 99%

Robust Hashing of Vector Data Using Generalized Curvatures of Polyline

Lee

Kwon

2013

IEICE Trans. Inf. & Syst.

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SUMMARYWith the rapid expansion of vector data model application to digital content such as drawings and digital maps, the security and retrieval for vector data models have become an issue. In this paper, we present a vector data-hashing algorithm for the authentication, copy protection, and indexing of vector data models that are composed of a number of layers in CAD family formats. The proposed hashing algorithm groups polylines in a vector data model and generates group coefficients by the curvatures of the first and second type of polylines. Subsequently, we calculate the feature coefficients by projecting the group coefficients onto a random pattern, and finally generate the binary hash from binarization of the feature coefficients. Based on experimental results using a number of drawings and digital maps, we verified the robustness of the proposed hashing algorithm against various attacks and the uniqueness and security of the random key.

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Robust and Secure Image Hashing via Non-Negative Matrix Factorizations

Cited by 265 publications

References 18 publications

A Survey on Image Hashing for Image Authentication

A Survey on Image Hashing for Image Authentication

The Robust Algorithm of 3D Medical Image Retrieval Based on Perceptual Hashing

Robust Hashing of Vector Data Using Generalized Curvatures of Polyline

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