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

Robust image registration using subspace method in Radon transform domain

Sensor Review ◽  
2019 ◽  
Vol 39 (5) ◽  
pp. 645-651
Author(s):  
Ning Wei ◽  
Yu He ◽  
Junqing Liu ◽  
Peng Chen
Keyword(s):  
Image Registration ◽  
Radon Transform ◽  
Median Filter ◽  
Least Square ◽  
Phase Correlation ◽  
Transform Domain ◽  
Registration Method ◽  
Content Type ◽  
Structural Differences ◽  
Robust Image

Purpose The purpose of this paper is to represent a robust image registration method to align noisy and deformed images in their Radon transform domain. Due to the limitation of imaging mechanism, the images are often highly noisy. Even worse, the objects in images have structural differences from time to time. Design/methodology/approach To eliminate these degressions, the proposed method is equipped with subspace-based power spectrum analysis algorithm for rotation estimation and a new global median filter least square algorithm for displacement computation. Findings Experiments on strongly noisy and degenerated images show that the proposed method exhibits better accuracy and robustness than phase correlation-based method. In addition, the method can also be applied to multi-modal registration, where the results are comparable to mutual information method but spending much less time. Originality/value A robust image registration method is proposed, which has better performance than traditional methods.

scholarly journals A Robust Image Watermarking Approach Using Cycle Variational Autoencoder

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Qiang Wei ◽  
Hu Wang ◽  
Gongxuan Zhang
Keyword(s):  
Copyright Protection ◽  
Cloud Storage ◽  
Image Watermarking ◽  
Rapid Development ◽  
Transform Domain ◽  
Watermark Image ◽  
Visual Effects ◽  
Variational Autoencoder ◽  
Robust Image ◽  
Traditional Approaches

With the rapid development of Internet and cloud storage, data security sharing and copyright protection are becoming more and more important. In this paper, we introduce a robust image watermarking algorithm for copyright protection based on variational autoencoder networks. The proposed image watermarking embedding and extracting network consists of three parts: encoder subnetwork, decoder subnetwork, and detector subnetwork. In the training process, the encoder and decoder subnetworks learn a robust image representation model and further implement the embedding of 1-bit watermark image to the cover image. Meanwhile, the detector subnetwork learns to extract the 1-bit watermark image from the embedding image. Experimental results demonstrate that the watermarked images generated by the proposed algorithm have better visual effects and are more robust against geometric and noise attacks than traditional approaches in the transform domain.

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.

Amplitude preservation of Radon-based multiple-removal filters

Geophysics ◽  
2006 ◽  
Vol 71 (5) ◽  
pp. V123-V126 ◽  
Author(s):  
Ethan J. Nowak ◽  
Matthias G. Imhof
Keyword(s):  
Radon Transform ◽  
Seismic Data ◽  
Transform Domain ◽  
Multiple Reflections ◽  
Avo Analysis ◽  
Amplitude Versus Offset ◽  
Amplitude Preservation ◽  
Amplitude Versus

This study examines the effect of filtering in the Radon transform domain on reflection amplitudes. Radon filters are often used for removal of multiple reflections from normal moveout-corrected seismic data. The unweighted solution to the Radon transform reduces reflection amplitudes at both near and far offsets due to a truncation effect. However, the weighted solutions to the transform produce localized events in the transform domain, which minimizes this truncation effect. Synthetic examples suggest that filters designed in the Radon domain based on a weighted solution to the linear, parabolic, or hyperbolic transforms preserve the near- and far-offset reflection amplitudes while removing the multiples; whereas the unweighted solutions diminish reflection amplitudes which may distort subsequent amplitude-versus-offset (AVO) analysis.

The Shuey-Radon transform

Geophysics ◽  
2019 ◽  
Vol 84 (3) ◽  
pp. V197-V206 ◽  
Author(s):  
Ali Gholami ◽  
Milad Farshad
Keyword(s):  
Radon Transform ◽  
Seismic Data ◽  
Matching Pursuit ◽  
Real Data ◽  
Transform Domain ◽  
Amplitude Variation ◽  
Amplitude Variation With Offset ◽  
Linear System Of Equations ◽  
Matching Pursuit Algorithm ◽  
Zero Offset

The traditional hyperbolic Radon transform (RT) decomposes seismic data into a sum of constant amplitude basis functions. This limits the performance of the transform when dealing with real data in which the reflection amplitudes include the amplitude variation with offset (AVO) variations. We adopted the Shuey-Radon transform as a combination of the RT and Shuey’s approximation of reflectivity to accurately model reflections including AVO effects. The new transform splits the seismic gather into three Radon panels: The first models the reflections at zero offset, and the other two panels add capability to model the AVO gradient and curvature. There are two main advantages of the Shuey-Radon transform over similar algorithms, which are based on a polynomial expansion of the AVO response. (1) It is able to model reflections more accurately. This leads to more focused coefficients in the transform domain and hence provides more accurate processing results. (2) Unlike polynomial-based approaches, the coefficients of the Shuey-Radon transform are directly connected to the classic AVO parameters (intercept, gradient, and curvature). Therefore, the resulting coefficients can further be used for interpretation purposes. The solution of the new transform is defined via an underdetermined linear system of equations. It is formulated as a sparsity-promoting optimization, and it is solved efficiently using an orthogonal matching pursuit algorithm. Applications to different numerical experiments indicate that the Shuey-Radon transform outperforms the polynomial and conventional RTs.

Wavelet-Radon domain dealiasing and interpolation of seismic data

Geophysics ◽  
2007 ◽  
Vol 72 (2) ◽  
pp. V41-V49 ◽  
Author(s):  
Zhou Yu ◽  
John Ferguson ◽  
George McMechan ◽  
Phil Anno
Keyword(s):  
Time Series ◽  
Radon Transform ◽  
Seismic Data ◽  
High Frequency ◽  
Transform Domain ◽  
Original Time Series ◽  
Spatial Aliasing ◽  
Multichannel Data ◽  
And Migration ◽  
Original Time

Spatial aliasing is unavoidable in some seismic data and has serious effects on the performance of multichannel data processing and migration. Antialias filtering produces distortion of the signal through the removal of high-frequency information. In contrast, dealiasing produces an unaliased estimate of the signal at all frequencies present in the original time series. A new dealiasing algorithm is developed by exploiting the properties of seismic wavefields in the wavelet-Radon transform domain, specifically the overlap of information between wavelet scales at the same frequency. The effectiveness of the wavelet-Radon dealiasing algorithm is demonstrated through the processing of both synthetic and field seismic data.

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