Robust Image Hash Based on Cyclic Coding the Distributed Features

2009 ◽  
Author(s):  
Sun Yang
Keyword(s):  
Image Hash ◽  
Robust Image

scholarly journals A Secure and Robust Image Hashing Scheme Using Gaussian Pyramids

Entropy ◽  
2019 ◽  
Vol 21 (11) ◽  
pp. 1132 ◽  
Author(s):  
Iram Bashir ◽  
Fawad Ahmed ◽  
Jawad Ahmad ◽  
Wadii Boulila ◽  
Nouf Alharbi
Keyword(s):  
False Negative ◽  
Hash Functions ◽  
Feature Space ◽  
Image Hashing ◽  
Gaussian Pyramid ◽  
Image Hash ◽  
Perceptual Hash ◽  
Robust Image ◽  
Robust Image Hashing ◽  
Cryptographic Hash Functions

Image hash is an alternative to cryptographic hash functions for checking integrity of digital images. Compared to cryptographic hash functions, an image hash or a Perceptual Hash Function (PHF) is resilient to content preserving distortions and sensitive to malicious tampering. In this paper, a robust and secure image hashing technique using a Gaussian pyramid is proposed. A Gaussian pyramid decomposes an image into different resolution levels which can be utilized to obtain robust and compact hash features. These stable features have been utilized in the proposed work to construct a secure and robust image hash. The proposed scheme uses Laplacian of Gaussian (LOG) and disk filters to filter the low-resolution Gaussian decomposed image. The filtered images are then subtracted and their difference is used as a hash. To make the hash secure, a key is introduced before feature extraction, thus making the entire feature space random. The proposed hashing scheme has been evaluated through a number of experiments involving cases of non-malicious distortions and malicious tampering. Experimental results reveal that the proposed hashing scheme is robust against non-malicious distortions and is sensitive to detect minute malicious tampering. Moreover, False Positive Probability (FPP) and False Negative Probability (FNP) results demonstrate the effectiveness of the proposed scheme when compared to state-of-the-art image hashing algorithms proposed in the literature.

Robust image hash function using local color features

2013 ◽  
Vol 67 (8) ◽  
pp. 717-722 ◽  
Author(s):  
Zhenjun Tang ◽  
Xianquan Zhang ◽  
Xuan Dai ◽  
Jianzhong Yang ◽  
Tianxiu Wu
Keyword(s):  
Hash Function ◽  
Local Color ◽  
Color Features ◽  
Image Hash ◽  
Robust Image

A Robust Image Perceptual Hashing Scheme Based on Improved Normalization Algorithm

2012 ◽  
Vol 532-533 ◽  
pp. 1389-1393
Author(s):  
Shu Sen Sun ◽  
Yong Zeng ◽  
Hua Xiong Zhang ◽  
Jiang Sheng Gui
Keyword(s):  
Discrete Cosine Transform ◽  
Normalization Method ◽  
Image Feature ◽  
Original Image ◽  
Affine Transformations ◽  
Image Normalization ◽  
Image Hash ◽  
Dct Coefficients ◽  
Perceptual Hashing ◽  
Robust Image

An improved image normalization algorithm is proposed firstly. Then a geometric robust image perceptual hashing scheme is proposed based on image normalization and discrete cosine transform. The original image is preprocessed using our improved image normalization method. And the selected DCT coefficients as image feature are encrypted for security. The geometric robust image hash is achieved by quantizing encrypted DCT coefficients and coding. The experimental results show that the algorithm can resist against common global affine transformations such as rotation, scaling, translation and their combinations.

Robust image hash in Radon transform domain for authentication

2011 ◽  
Vol 26 (6) ◽  
pp. 280-288 ◽  
Author(s):  
Yanqiang Lei ◽  
Yuangen Wang ◽  
Jiwu Huang
Keyword(s):  
Radon Transform ◽  
Transform Domain ◽  
Image Hash ◽  
Robust Image

Multiresolution Bio-Medical Image Segmentation using Fuzzy C-Means Clustering

2019 ◽  
Vol 8 (4) ◽  
pp. 9548-9551
Keyword(s):  
Image Segmentation ◽  
Medical Image Segmentation ◽  
Experimental Result ◽  
Segmentation Method ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Robust Image ◽  
Multiple Clusters ◽  
Single Pixel ◽  
Popular Image

Fuzzy c-means clustering is a popular image segmentation technique, in which a single pixel belongs to multiple clusters, with varying degree of membership. The main drawback of this method is it sensitive to noise. This method can be improved by incorporating multiresolution stationary wavelet analysis. In this paper we develop a robust image segmentation method using Fuzzy c-means clustering and wavelet transform. The experimental result shows that the proposed method is more accurate than the Fuzzy c-means clustering.

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