Fourier-Mellin transform for Robust Image Hashing

2013 ◽  
Author(s):  
Supakorn Prungsinchai ◽  
Fouad Khelifi ◽  
Ahmed Bouridane
Keyword(s):  
Mellin Transform ◽  
Image Hashing ◽  
Robust Image ◽  
Fourier Mellin Transform ◽  
Robust Image Hashing

scholarly journals Robust image hashing with compressed sensing and ordinal measures

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Zhenjun Tang ◽  
Hanyun Zhang ◽  
Shenglian Lu ◽  
Heng Yao ◽  
Xianquan Zhang
Keyword(s):  
Compressed Sensing ◽  
Image Hashing ◽  
Robust Image ◽  
Robust Image Hashing

Robust image hashing with embedding vector variance of LLE

2015 ◽  
Vol 43 ◽  
pp. 17-27 ◽  
Author(s):  
Zhenjun Tang ◽  
Linlin Ruan ◽  
Chuan Qin ◽  
Xianquan Zhang ◽  
Chunqiang Yu
Keyword(s):  
Image Hashing ◽  
Robust Image ◽  
Robust Image Hashing

Robust Image Hashing Based Efficient Authentication for Smart Industrial Environment

2019 ◽  
Vol 15 (12) ◽  
pp. 6541-6550 ◽  
Author(s):  
Muhammad Sajjad ◽  
Ijaz Ul Haq ◽  
Jaime Lloret ◽  
Weiping Ding ◽  
Khan Muhammad
Keyword(s):  
Image Hashing ◽  
Industrial Environment ◽  
Robust Image ◽  
Robust Image Hashing

Robust Hash for Detecting and Localizing Image Tampering

2010 ◽  
Vol 439-440 ◽  
pp. 1018-1023
Author(s):  
De Long Cui ◽  
Yong Fu Liu ◽  
Jing Long Zuo
Keyword(s):  
Fourier Transform ◽  
Fractional Fourier Transform ◽  
Image Hashing ◽  
Post Processing ◽  
Feature Extracting ◽  
Image Tampering ◽  
Secret Keys ◽  
Robust Image ◽  
Hashing Algorithm ◽  
Robust Image Hashing

In order to improve the sensitive to illegal manipulations of image hashing, a novel robust image hashing algorithm based on fractional Fourier transform (FRFT) for detecting and localizing image tampering is proposed in this paper. The framework of generating an image hashing includes three steps: preprocessing, feature extracting and post processing. The robust hashing sequence is obtained by FRFT coefficients of image blocks. The security of proposed algorithm is totally depended on the orders of FRFT which are saved as secret keys. Experimental results show that the proposed scheme is robust against perceptually acceptable modifications to the image such as JPEG compression, mid-filtering, and rotation, more importantly the tampered place can be identified accurately.

scholarly journals Robust Image Hashing Using Radon Transform and Invariant Features

2016 ◽  
Vol 25 (3) ◽  
pp. 556-564 ◽  
Author(s):  
Y. L. Liu ◽  
G. J. Xin ◽  
Y. Xiao
Keyword(s):  
Radon Transform ◽  
Image Hashing ◽  
Invariant Features ◽  
Robust Image ◽  
Robust Image Hashing

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.

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